{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loan Default Prediction\n",
    "## Team Members:\n",
    "* Harish Puvvada         - hp1047\n",
    "* Vamsi Mohan Ramineedi  - vmr286\n",
    "\n",
    "Github link: https://github.com/harishpuvvada/LoanDefault-Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import preprocessing,metrics \n",
    "from IPython.core.display import HTML\n",
    "pd.set_option(\"display.max_columns\",75)\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "from sklearn import linear_model,svm\n",
    "from sklearn.metrics import average_precision_score\n",
    "from sklearn.metrics import precision_recall_curve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "<span style=\"color:blue\">\n",
    "> Importing data of Lending club for the years 2012-14\n",
    "</span>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2012_13 = pd.read_csv(os.getenv('FDS')+'LoanStats_2012_to_2013.csv',low_memory=False,skiprows=1)\n",
    "df2014 = pd.read_csv(os.getenv('FDS')+'LoanStats_2014.csv',low_memory=False,skiprows=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "<span style=\"color:blue\">\n",
    "> Merged datasets of 2012-14 <br>\n",
    "> Removed all empty columns ( these are the columns with personal data of the borrowers. These are not disclosed by the company. so we dropped them)<br>\n",
    "> Target variable(Borrower is a Loan defaulter) - encoded to 0 or 1<br>\n",
    "</span>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.concat([df2012_13, df2014]) #merging 2012 to 2014 datasets\n",
    "dataset = dataset.iloc[:,2:111]          #removing empty columns\n",
    "empty_cols = [i for i in range(45,72)]   #more empty columns\n",
    "dataset = dataset.drop(dataset.columns[empty_cols],axis=1)\n",
    "data_with_loanstatus_sliced = dataset[(dataset['loan_status']==\"Fully Paid\") | (dataset['loan_status']==\"Charged Off\")]\n",
    "di = {\"Fully Paid\":0, \"Charged Off\":1}   #converting target variable to boolean\n",
    "Dataset_withBoolTarget= data_with_loanstatus_sliced.replace({\"loan_status\": di})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current shape of dataset : (376233, 82)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>emp_title</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>verification_status</th>\n",
       "      <th>issue_d</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>pymnt_plan</th>\n",
       "      <th>url</th>\n",
       "      <th>desc</th>\n",
       "      <th>purpose</th>\n",
       "      <th>title</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>addr_state</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>earliest_cr_line</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>mths_since_last_delinq</th>\n",
       "      <th>mths_since_last_record</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>initial_list_status</th>\n",
       "      <th>out_prncp</th>\n",
       "      <th>out_prncp_inv</th>\n",
       "      <th>total_pymnt</th>\n",
       "      <th>...</th>\n",
       "      <th>acc_open_past_24mths</th>\n",
       "      <th>avg_cur_bal</th>\n",
       "      <th>bc_open_to_buy</th>\n",
       "      <th>bc_util</th>\n",
       "      <th>chargeoff_within_12_mths</th>\n",
       "      <th>delinq_amnt</th>\n",
       "      <th>mo_sin_old_il_acct</th>\n",
       "      <th>mo_sin_old_rev_tl_op</th>\n",
       "      <th>mo_sin_rcnt_rev_tl_op</th>\n",
       "      <th>mo_sin_rcnt_tl</th>\n",
       "      <th>mort_acc</th>\n",
       "      <th>mths_since_recent_bc</th>\n",
       "      <th>mths_since_recent_bc_dlq</th>\n",
       "      <th>mths_since_recent_inq</th>\n",
       "      <th>mths_since_recent_revol_delinq</th>\n",
       "      <th>num_accts_ever_120_pd</th>\n",
       "      <th>num_actv_bc_tl</th>\n",
       "      <th>num_actv_rev_tl</th>\n",
       "      <th>num_bc_sats</th>\n",
       "      <th>num_bc_tl</th>\n",
       "      <th>num_il_tl</th>\n",
       "      <th>num_op_rev_tl</th>\n",
       "      <th>num_rev_accts</th>\n",
       "      <th>num_rev_tl_bal_gt_0</th>\n",
       "      <th>num_sats</th>\n",
       "      <th>num_tl_120dpd_2m</th>\n",
       "      <th>num_tl_30dpd</th>\n",
       "      <th>num_tl_90g_dpd_24m</th>\n",
       "      <th>num_tl_op_past_12m</th>\n",
       "      <th>pct_tl_nvr_dlq</th>\n",
       "      <th>percent_bc_gt_75</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <th>tax_liens</th>\n",
       "      <th>tot_hi_cred_lim</th>\n",
       "      <th>total_bal_ex_mort</th>\n",
       "      <th>total_bc_limit</th>\n",
       "      <th>total_il_high_credit_limit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27050.0</td>\n",
       "      <td>27050.0</td>\n",
       "      <td>27050.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>10.99%</td>\n",
       "      <td>885.46</td>\n",
       "      <td>B</td>\n",
       "      <td>B2</td>\n",
       "      <td>Team Leadern Customer Ops &amp; Systems</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>OWN</td>\n",
       "      <td>55000.0</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Dec-2013</td>\n",
       "      <td>0</td>\n",
       "      <td>n</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Borrower added on 12/31/13 &gt; Combining high ...</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>Debt Consolidation</td>\n",
       "      <td>481xx</td>\n",
       "      <td>MI</td>\n",
       "      <td>22.87</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Oct-1986</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36638.0</td>\n",
       "      <td>61.2%</td>\n",
       "      <td>27.0</td>\n",
       "      <td>w</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31752.530000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9570.0</td>\n",
       "      <td>16473.0</td>\n",
       "      <td>53.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>326.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>138554.0</td>\n",
       "      <td>70186.0</td>\n",
       "      <td>35700.0</td>\n",
       "      <td>33054.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12000.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>10.99%</td>\n",
       "      <td>392.81</td>\n",
       "      <td>B</td>\n",
       "      <td>B2</td>\n",
       "      <td>Project Manager</td>\n",
       "      <td>4 years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Dec-2013</td>\n",
       "      <td>0</td>\n",
       "      <td>n</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Borrower added on 12/31/13 &gt; I would like to...</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>No Regrets</td>\n",
       "      <td>281xx</td>\n",
       "      <td>NC</td>\n",
       "      <td>4.62</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dec-2009</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7137.0</td>\n",
       "      <td>24%</td>\n",
       "      <td>18.0</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13988.609996</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>476.0</td>\n",
       "      <td>15216.0</td>\n",
       "      <td>15.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29700.0</td>\n",
       "      <td>7137.0</td>\n",
       "      <td>18100.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4800.0</td>\n",
       "      <td>4800.0</td>\n",
       "      <td>4800.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>10.99%</td>\n",
       "      <td>157.13</td>\n",
       "      <td>B</td>\n",
       "      <td>B2</td>\n",
       "      <td>Surgical Technician</td>\n",
       "      <td>2 years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>39600.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Dec-2013</td>\n",
       "      <td>0</td>\n",
       "      <td>n</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Borrower added on 12/31/13 &gt; Just bought a h...</td>\n",
       "      <td>home_improvement</td>\n",
       "      <td>For The House</td>\n",
       "      <td>782xx</td>\n",
       "      <td>TX</td>\n",
       "      <td>2.49</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Aug-1995</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4136.0</td>\n",
       "      <td>16.1%</td>\n",
       "      <td>8.0</td>\n",
       "      <td>w</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5157.519457</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1379.0</td>\n",
       "      <td>21564.0</td>\n",
       "      <td>16.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25700.0</td>\n",
       "      <td>4136.0</td>\n",
       "      <td>25700.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 82 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_amnt  funded_amnt  funded_amnt_inv        term int_rate  installment  \\\n",
       "0    27050.0      27050.0          27050.0   36 months   10.99%       885.46   \n",
       "1    12000.0      12000.0          12000.0   36 months   10.99%       392.81   \n",
       "2     4800.0       4800.0           4800.0   36 months   10.99%       157.13   \n",
       "\n",
       "  grade sub_grade                            emp_title emp_length  \\\n",
       "0     B        B2  Team Leadern Customer Ops & Systems  10+ years   \n",
       "1     B        B2                      Project Manager    4 years   \n",
       "2     B        B2                  Surgical Technician    2 years   \n",
       "\n",
       "  home_ownership  annual_inc verification_status   issue_d  loan_status  \\\n",
       "0            OWN     55000.0            Verified  Dec-2013            0   \n",
       "1           RENT     60000.0        Not Verified  Dec-2013            0   \n",
       "2       MORTGAGE     39600.0     Source Verified  Dec-2013            0   \n",
       "\n",
       "  pymnt_plan  url                                               desc  \\\n",
       "0          n  NaN    Borrower added on 12/31/13 > Combining high ...   \n",
       "1          n  NaN    Borrower added on 12/31/13 > I would like to...   \n",
       "2          n  NaN    Borrower added on 12/31/13 > Just bought a h...   \n",
       "\n",
       "              purpose               title zip_code addr_state    dti  \\\n",
       "0  debt_consolidation  Debt Consolidation    481xx         MI  22.87   \n",
       "1  debt_consolidation          No Regrets    281xx         NC   4.62   \n",
       "2    home_improvement       For The House    782xx         TX   2.49   \n",
       "\n",
       "   delinq_2yrs earliest_cr_line  inq_last_6mths  mths_since_last_delinq  \\\n",
       "0          0.0         Oct-1986             0.0                     NaN   \n",
       "1          0.0         Dec-2009             1.0                     NaN   \n",
       "2          0.0         Aug-1995             2.0                     NaN   \n",
       "\n",
       "   mths_since_last_record  open_acc  pub_rec  revol_bal revol_util  total_acc  \\\n",
       "0                     NaN      14.0      0.0    36638.0      61.2%       27.0   \n",
       "1                     NaN      15.0      0.0     7137.0        24%       18.0   \n",
       "2                     NaN       3.0      0.0     4136.0      16.1%        8.0   \n",
       "\n",
       "  initial_list_status  out_prncp  out_prncp_inv   total_pymnt  \\\n",
       "0                   w        0.0            0.0  31752.530000   \n",
       "1                   f        0.0            0.0  13988.609996   \n",
       "2                   w        0.0            0.0   5157.519457   \n",
       "\n",
       "              ...              acc_open_past_24mths  avg_cur_bal  \\\n",
       "0             ...                               3.0       9570.0   \n",
       "1             ...                               8.0        476.0   \n",
       "2             ...                               0.0       1379.0   \n",
       "\n",
       "   bc_open_to_buy  bc_util  chargeoff_within_12_mths  delinq_amnt  \\\n",
       "0         16473.0     53.9                       0.0          0.0   \n",
       "1         15216.0     15.9                       0.0          0.0   \n",
       "2         21564.0     16.1                       0.0          0.0   \n",
       "\n",
       "  mo_sin_old_il_acct  mo_sin_old_rev_tl_op  mo_sin_rcnt_rev_tl_op  \\\n",
       "0              117.0                 326.0                   16.0   \n",
       "1                NaN                  48.0                    1.0   \n",
       "2              104.0                 220.0                   25.0   \n",
       "\n",
       "   mo_sin_rcnt_tl  mort_acc  mths_since_recent_bc  mths_since_recent_bc_dlq  \\\n",
       "0             6.0       4.0                  16.0                       NaN   \n",
       "1             1.0       0.0                   1.0                       NaN   \n",
       "2            25.0       0.0                  25.0                       NaN   \n",
       "\n",
       "   mths_since_recent_inq  mths_since_recent_revol_delinq  \\\n",
       "0                    8.0                             NaN   \n",
       "1                    3.0                             NaN   \n",
       "2                    3.0                             NaN   \n",
       "\n",
       "   num_accts_ever_120_pd  num_actv_bc_tl  num_actv_rev_tl  num_bc_sats  \\\n",
       "0                    0.0             2.0              4.0          4.0   \n",
       "1                    0.0             4.0              7.0          8.0   \n",
       "2                    0.0             2.0              2.0          3.0   \n",
       "\n",
       "   num_bc_tl  num_il_tl  num_op_rev_tl  num_rev_accts  num_rev_tl_bal_gt_0  \\\n",
       "0        8.0        8.0           10.0           15.0                  4.0   \n",
       "1       10.0        0.0           15.0           18.0                  7.0   \n",
       "2        4.0        1.0            3.0            7.0                  2.0   \n",
       "\n",
       "   num_sats  num_tl_120dpd_2m  num_tl_30dpd  num_tl_90g_dpd_24m  \\\n",
       "0      14.0               0.0           0.0                 0.0   \n",
       "1      15.0               0.0           0.0                 0.0   \n",
       "2       3.0               0.0           0.0                 0.0   \n",
       "\n",
       "   num_tl_op_past_12m  pct_tl_nvr_dlq  percent_bc_gt_75  pub_rec_bankruptcies  \\\n",
       "0                 1.0           100.0              25.0                   0.0   \n",
       "1                 4.0           100.0               0.0                   0.0   \n",
       "2                 0.0           100.0               0.0                   0.0   \n",
       "\n",
       "   tax_liens  tot_hi_cred_lim  total_bal_ex_mort  total_bc_limit  \\\n",
       "0        0.0         138554.0            70186.0         35700.0   \n",
       "1        0.0          29700.0             7137.0         18100.0   \n",
       "2        0.0          25700.0             4136.0         25700.0   \n",
       "\n",
       "   total_il_high_credit_limit  \n",
       "0                     33054.0  \n",
       "1                         0.0  \n",
       "2                         0.0  \n",
       "\n",
       "[3 rows x 82 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Dataset_withBoolTarget['loan_status'].value_counts()\n",
    "print(\"Current shape of dataset :\",Dataset_withBoolTarget.shape)\n",
    "Dataset_withBoolTarget.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current shape of dataset : (376233, 74)\n"
     ]
    }
   ],
   "source": [
    "dataset=Dataset_withBoolTarget.dropna(thresh = 340000,axis=1) #340000 is minimum number of non-NA values\n",
    "print(\"Current shape of dataset :\",dataset.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "<span style=\"color:blue\">\n",
    "> some more columns were dropped in the below cell. some of them are not related to our target variable and some of them are redundant. <br>\n",
    "</span>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current shape of dataset : (376233, 52)\n"
     ]
    }
   ],
   "source": [
    "del_col_names = [\"delinq_2yrs\",  \"last_pymnt_d\", \"chargeoff_within_12_mths\",\"delinq_amnt\",\"emp_title\", \"term\", \"emp_title\", \"pymnt_plan\",\"purpose\",\"title\", \"zip_code\", \"verification_status\", \"dti\",\"earliest_cr_line\", \"initial_list_status\", \"out_prncp\",\n",
    "\"pymnt_plan\", \"num_tl_90g_dpd_24m\", \"num_tl_30dpd\", \"num_tl_120dpd_2m\", \"num_accts_ever_120_pd\", \"delinq_amnt\", \n",
    "\"chargeoff_within_12_mths\", \"total_rec_late_fee\", \"out_prncp_inv\", \"issue_d\"] #deleting some more columns\n",
    "dataset = dataset.drop(labels = del_col_names, axis = 1) \n",
    "print(\"Current shape of dataset :\",dataset.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "<span style=\"color:blue\">\n",
    "> After indepth research about these rest 52 features, We selected ~20 relevant features using correlation matrix values. After few cells (in this ipython notebook), we used \"RFE (Recursive Feature Elimination)\" and \" PCA(Principle Component Analysis)\" do the actual feature selection.<br>\n",
    "</span>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current shape of dataset : (376233, 18)\n"
     ]
    }
   ],
   "source": [
    "features = ['funded_amnt','emp_length','annual_inc','home_ownership','grade',\n",
    "            \"last_pymnt_amnt\", \"mort_acc\", \"pub_rec\", \"int_rate\", \"open_acc\",\"num_actv_rev_tl\",\n",
    "            \"mo_sin_rcnt_rev_tl_op\",\"mo_sin_old_rev_tl_op\",\"bc_util\",\"bc_open_to_buy\",\n",
    "            \"avg_cur_bal\",\"acc_open_past_24mths\",'loan_status'] #'sub_grade' #selecting final features #'addr_state''tax_liens',\n",
    "Final_data = dataset[features] #19 features with target var\n",
    "Final_data[\"int_rate\"] = Final_data[\"int_rate\"].apply(lambda x:float(x[:-1]) ) #reomving % sign, conv to float  - int_rate column\n",
    "Final_data= Final_data.reset_index(drop=True)\n",
    "print(\"Current shape of dataset :\",Final_data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Transformation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "<span style=\"color:blue\">\n",
    "> Grade - Borrower's grade given basing on his/her past history - encoded to numerical values. <br>\n",
    "> home_ownership - this is feature in the dataset which had to be encoded to numerical values. <br>\n",
    "> Emp_Length - this feature was not formatted properly. It has some values which was in the format like \"10+years\",\"5years\"...etc. we changed them to numerical values in the below cell.\n",
    "</span>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current shape of dataset : (376233, 18)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>grade</th>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <th>mort_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>num_actv_rev_tl</th>\n",
       "      <th>mo_sin_rcnt_rev_tl_op</th>\n",
       "      <th>mo_sin_old_rev_tl_op</th>\n",
       "      <th>bc_util</th>\n",
       "      <th>bc_open_to_buy</th>\n",
       "      <th>avg_cur_bal</th>\n",
       "      <th>acc_open_past_24mths</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27050.0</td>\n",
       "      <td>10</td>\n",
       "      <td>55000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>6074.19</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.99</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>326.0</td>\n",
       "      <td>53.9</td>\n",
       "      <td>16473.0</td>\n",
       "      <td>9570.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>3775.55</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.99</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>15.9</td>\n",
       "      <td>15216.0</td>\n",
       "      <td>476.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4800.0</td>\n",
       "      <td>2</td>\n",
       "      <td>39600.0</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>3900.48</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.99</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>16.1</td>\n",
       "      <td>21564.0</td>\n",
       "      <td>1379.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12000.0</td>\n",
       "      <td>10</td>\n",
       "      <td>130000.0</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>398.28</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.99</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>3567.0</td>\n",
       "      <td>36362.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7550.0</td>\n",
       "      <td>3</td>\n",
       "      <td>28000.0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>529.67</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.24</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   funded_amnt  emp_length  annual_inc  home_ownership  grade  \\\n",
       "0      27050.0          10     55000.0               4      6   \n",
       "1      12000.0           4     60000.0               5      6   \n",
       "2       4800.0           2     39600.0               6      6   \n",
       "3      12000.0          10    130000.0               6      6   \n",
       "4       7550.0           3     28000.0               5      5   \n",
       "\n",
       "   last_pymnt_amnt  mort_acc  pub_rec  int_rate  open_acc  num_actv_rev_tl  \\\n",
       "0          6074.19       4.0      0.0     10.99      14.0              4.0   \n",
       "1          3775.55       0.0      0.0     10.99      15.0              7.0   \n",
       "2          3900.48       0.0      0.0     10.99       3.0              2.0   \n",
       "3           398.28       3.0      0.0     11.99       9.0              5.0   \n",
       "4           529.67       0.0      0.0     16.24       4.0              4.0   \n",
       "\n",
       "   mo_sin_rcnt_rev_tl_op  mo_sin_old_rev_tl_op  bc_util  bc_open_to_buy  \\\n",
       "0                   16.0                 326.0     53.9         16473.0   \n",
       "1                    1.0                  48.0     15.9         15216.0   \n",
       "2                   25.0                 220.0     16.1         21564.0   \n",
       "3                    4.0                 193.0     93.0          3567.0   \n",
       "4                   17.0                  38.0     96.0           160.0   \n",
       "\n",
       "   avg_cur_bal  acc_open_past_24mths  loan_status  \n",
       "0       9570.0                   3.0            0  \n",
       "1        476.0                   8.0            0  \n",
       "2       1379.0                   0.0            0  \n",
       "3      36362.0                   4.0            0  \n",
       "4       1440.0                   1.0            0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Data encoding\n",
    "Final_data['grade'] = Final_data['grade'].map({'A':7,'B':6,'C':5,'D':4,'E':3,'F':2,'G':1})\n",
    "Final_data[\"home_ownership\"] = Final_data[\"home_ownership\"].map({\"MORTGAGE\":6,\"RENT\":5,\"OWN\":4,\"OTHER\":3,\"NONE\":2,\"ANY\":1})\n",
    "Final_data[\"emp_length\"] = Final_data[\"emp_length\"].replace({'years':'','year':'',' ':'','<':'','\\+':'','n/a':'0'}, regex = True)\n",
    "Final_data[\"emp_length\"] = Final_data[\"emp_length\"].apply(lambda x:int(x))\n",
    "print(\"Current shape of dataset :\",Final_data.shape)\n",
    "Final_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filling Missing values and Feature scaling \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "<span style=\"color:blue\">\n",
    "> We have some important features which have some missing values. We filled those missing those values with the mean of the column. <br>\n",
    "> We scaled the features all the features here using standard scaler. <br>\n",
    "> We sampled our dataset here after infering from the learning curve plotted.\n",
    "</span>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current shape of dataset : (376233, 18)\n"
     ]
    }
   ],
   "source": [
    "Final_data.fillna(Final_data.mean(),inplace = True)\n",
    "HTML(Final_data.tail().to_html())\n",
    "print(\"Current shape of dataset :\",Final_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    310035\n",
       "1     66198\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scl = preprocessing.StandardScaler() #instance of preprocessing\n",
    "fields = Final_data.columns.values[:-1]\n",
    "data_clean = pd.DataFrame(scl.fit_transform(Final_data[fields]), columns = fields)\n",
    "data_clean['loan_status'] = Final_data['loan_status']\n",
    "data_clean['loan_status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current shape of dataset : (11000, 18)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>grade</th>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <th>mort_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>num_actv_rev_tl</th>\n",
       "      <th>mo_sin_rcnt_rev_tl_op</th>\n",
       "      <th>mo_sin_old_rev_tl_op</th>\n",
       "      <th>bc_util</th>\n",
       "      <th>bc_open_to_buy</th>\n",
       "      <th>avg_cur_bal</th>\n",
       "      <th>acc_open_past_24mths</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.506304</td>\n",
       "      <td>-1.053656</td>\n",
       "      <td>0.959561</td>\n",
       "      <td>-0.634134</td>\n",
       "      <td>1.362088</td>\n",
       "      <td>-0.166336</td>\n",
       "      <td>-0.376315</td>\n",
       "      <td>-0.323888</td>\n",
       "      <td>-1.418502</td>\n",
       "      <td>0.341840</td>\n",
       "      <td>-0.584087</td>\n",
       "      <td>0.049333</td>\n",
       "      <td>0.323170</td>\n",
       "      <td>-1.435731</td>\n",
       "      <td>0.270224</td>\n",
       "      <td>0.578650</td>\n",
       "      <td>-0.445595</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.201260</td>\n",
       "      <td>1.140751</td>\n",
       "      <td>0.237770</td>\n",
       "      <td>0.899053</td>\n",
       "      <td>1.362088</td>\n",
       "      <td>-0.497044</td>\n",
       "      <td>0.087642</td>\n",
       "      <td>-0.323888</td>\n",
       "      <td>-1.242415</td>\n",
       "      <td>-0.061440</td>\n",
       "      <td>0.448250</td>\n",
       "      <td>-0.082104</td>\n",
       "      <td>0.185550</td>\n",
       "      <td>0.945755</td>\n",
       "      <td>-0.597837</td>\n",
       "      <td>0.004462</td>\n",
       "      <td>-0.445595</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.384286</td>\n",
       "      <td>-1.327957</td>\n",
       "      <td>-0.706111</td>\n",
       "      <td>-0.634134</td>\n",
       "      <td>-0.950935</td>\n",
       "      <td>-0.684147</td>\n",
       "      <td>-0.840272</td>\n",
       "      <td>-0.323888</td>\n",
       "      <td>0.564197</td>\n",
       "      <td>1.551678</td>\n",
       "      <td>-0.239975</td>\n",
       "      <td>1.495137</td>\n",
       "      <td>-1.156245</td>\n",
       "      <td>0.807697</td>\n",
       "      <td>-0.579249</td>\n",
       "      <td>-0.663657</td>\n",
       "      <td>0.638473</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.262269</td>\n",
       "      <td>1.140751</td>\n",
       "      <td>-0.058350</td>\n",
       "      <td>0.899053</td>\n",
       "      <td>0.591081</td>\n",
       "      <td>0.089108</td>\n",
       "      <td>1.479513</td>\n",
       "      <td>1.571072</td>\n",
       "      <td>-0.613531</td>\n",
       "      <td>0.341840</td>\n",
       "      <td>1.136474</td>\n",
       "      <td>-0.016386</td>\n",
       "      <td>0.036462</td>\n",
       "      <td>0.520078</td>\n",
       "      <td>-0.411503</td>\n",
       "      <td>0.689425</td>\n",
       "      <td>-0.084239</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.457635</td>\n",
       "      <td>0.592149</td>\n",
       "      <td>-0.243424</td>\n",
       "      <td>-0.634134</td>\n",
       "      <td>0.591081</td>\n",
       "      <td>0.950519</td>\n",
       "      <td>-0.840272</td>\n",
       "      <td>-0.323888</td>\n",
       "      <td>-1.064040</td>\n",
       "      <td>1.753318</td>\n",
       "      <td>3.889373</td>\n",
       "      <td>-0.344978</td>\n",
       "      <td>0.391980</td>\n",
       "      <td>-1.113598</td>\n",
       "      <td>0.837377</td>\n",
       "      <td>-0.803941</td>\n",
       "      <td>-1.168306</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   funded_amnt  emp_length  annual_inc  home_ownership     grade  \\\n",
       "0    -0.506304   -1.053656    0.959561       -0.634134  1.362088   \n",
       "1    -0.201260    1.140751    0.237770        0.899053  1.362088   \n",
       "2    -0.384286   -1.327957   -0.706111       -0.634134 -0.950935   \n",
       "3    -0.262269    1.140751   -0.058350        0.899053  0.591081   \n",
       "4     0.457635    0.592149   -0.243424       -0.634134  0.591081   \n",
       "\n",
       "   last_pymnt_amnt  mort_acc   pub_rec  int_rate  open_acc  num_actv_rev_tl  \\\n",
       "0        -0.166336 -0.376315 -0.323888 -1.418502  0.341840        -0.584087   \n",
       "1        -0.497044  0.087642 -0.323888 -1.242415 -0.061440         0.448250   \n",
       "2        -0.684147 -0.840272 -0.323888  0.564197  1.551678        -0.239975   \n",
       "3         0.089108  1.479513  1.571072 -0.613531  0.341840         1.136474   \n",
       "4         0.950519 -0.840272 -0.323888 -1.064040  1.753318         3.889373   \n",
       "\n",
       "   mo_sin_rcnt_rev_tl_op  mo_sin_old_rev_tl_op   bc_util  bc_open_to_buy  \\\n",
       "0               0.049333              0.323170 -1.435731        0.270224   \n",
       "1              -0.082104              0.185550  0.945755       -0.597837   \n",
       "2               1.495137             -1.156245  0.807697       -0.579249   \n",
       "3              -0.016386              0.036462  0.520078       -0.411503   \n",
       "4              -0.344978              0.391980 -1.113598        0.837377   \n",
       "\n",
       "   avg_cur_bal  acc_open_past_24mths  loan_status  \n",
       "0     0.578650             -0.445595            0  \n",
       "1     0.004462             -0.445595            0  \n",
       "2    -0.663657              0.638473            1  \n",
       "3     0.689425             -0.084239            0  \n",
       "4    -0.803941             -1.168306            0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loanstatus_0 = data_clean[data_clean[\"loan_status\"]==0]\n",
    "loanstatus_1 = data_clean[data_clean[\"loan_status\"]==1]\n",
    "subset_of_loanstatus_0 = loanstatus_0.sample(n=5500)\n",
    "subset_of_loanstatus_1 = loanstatus_1.sample(n=5500)\n",
    "data_clean = pd.concat([subset_of_loanstatus_1, subset_of_loanstatus_0])\n",
    "data_clean = data_clean.sample(frac=1).reset_index(drop=True)\n",
    "print(\"Current shape of dataset :\",data_clean.shape)\n",
    "data_clean.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "<span style=\"color:blue\">\n",
    "Below are correlation values between the features finally selected.\n",
    "</span>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>grade</th>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <th>mort_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>num_actv_rev_tl</th>\n",
       "      <th>mo_sin_rcnt_rev_tl_op</th>\n",
       "      <th>mo_sin_old_rev_tl_op</th>\n",
       "      <th>bc_util</th>\n",
       "      <th>bc_open_to_buy</th>\n",
       "      <th>avg_cur_bal</th>\n",
       "      <th>acc_open_past_24mths</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>funded_amnt</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.135988</td>\n",
       "      <td>0.425629</td>\n",
       "      <td>0.187621</td>\n",
       "      <td>-0.181152</td>\n",
       "      <td>0.336927</td>\n",
       "      <td>0.230845</td>\n",
       "      <td>-0.089864</td>\n",
       "      <td>0.178345</td>\n",
       "      <td>0.217118</td>\n",
       "      <td>0.147857</td>\n",
       "      <td>0.051495</td>\n",
       "      <td>0.166818</td>\n",
       "      <td>0.039460</td>\n",
       "      <td>0.194762</td>\n",
       "      <td>0.235574</td>\n",
       "      <td>0.022169</td>\n",
       "      <td>0.079202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emp_length</th>\n",
       "      <td>0.135988</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.115969</td>\n",
       "      <td>0.137079</td>\n",
       "      <td>-0.019547</td>\n",
       "      <td>0.057445</td>\n",
       "      <td>0.166725</td>\n",
       "      <td>0.016200</td>\n",
       "      <td>0.023258</td>\n",
       "      <td>0.067730</td>\n",
       "      <td>0.115884</td>\n",
       "      <td>0.002138</td>\n",
       "      <td>0.118255</td>\n",
       "      <td>0.041380</td>\n",
       "      <td>0.015400</td>\n",
       "      <td>0.100102</td>\n",
       "      <td>0.036747</td>\n",
       "      <td>-0.005707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>annual_inc</th>\n",
       "      <td>0.425629</td>\n",
       "      <td>0.115969</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.192284</td>\n",
       "      <td>0.039387</td>\n",
       "      <td>0.201918</td>\n",
       "      <td>0.280073</td>\n",
       "      <td>-0.017884</td>\n",
       "      <td>-0.046836</td>\n",
       "      <td>0.164285</td>\n",
       "      <td>0.058023</td>\n",
       "      <td>0.039310</td>\n",
       "      <td>0.163209</td>\n",
       "      <td>-0.000826</td>\n",
       "      <td>0.166425</td>\n",
       "      <td>0.392761</td>\n",
       "      <td>0.063209</td>\n",
       "      <td>-0.086017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>home_ownership</th>\n",
       "      <td>0.187621</td>\n",
       "      <td>0.137079</td>\n",
       "      <td>0.192284</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.046116</td>\n",
       "      <td>0.126158</td>\n",
       "      <td>0.408592</td>\n",
       "      <td>-0.001365</td>\n",
       "      <td>-0.044420</td>\n",
       "      <td>0.118312</td>\n",
       "      <td>0.039594</td>\n",
       "      <td>0.019183</td>\n",
       "      <td>0.103986</td>\n",
       "      <td>0.009546</td>\n",
       "      <td>0.076216</td>\n",
       "      <td>0.395941</td>\n",
       "      <td>0.052456</td>\n",
       "      <td>-0.058254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade</th>\n",
       "      <td>-0.181152</td>\n",
       "      <td>-0.019547</td>\n",
       "      <td>0.039387</td>\n",
       "      <td>0.046116</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.001873</td>\n",
       "      <td>0.077550</td>\n",
       "      <td>-0.056106</td>\n",
       "      <td>-0.962508</td>\n",
       "      <td>-0.017717</td>\n",
       "      <td>-0.115849</td>\n",
       "      <td>0.102423</td>\n",
       "      <td>0.103569</td>\n",
       "      <td>-0.290733</td>\n",
       "      <td>0.279636</td>\n",
       "      <td>0.088589</td>\n",
       "      <td>-0.171499</td>\n",
       "      <td>-0.314444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <td>0.336927</td>\n",
       "      <td>0.057445</td>\n",
       "      <td>0.201918</td>\n",
       "      <td>0.126158</td>\n",
       "      <td>-0.001873</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.157891</td>\n",
       "      <td>-0.035484</td>\n",
       "      <td>-0.006573</td>\n",
       "      <td>0.070983</td>\n",
       "      <td>0.000549</td>\n",
       "      <td>0.024228</td>\n",
       "      <td>0.085405</td>\n",
       "      <td>-0.050633</td>\n",
       "      <td>0.136156</td>\n",
       "      <td>0.155880</td>\n",
       "      <td>0.012274</td>\n",
       "      <td>-0.478829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mort_acc</th>\n",
       "      <td>0.230845</td>\n",
       "      <td>0.166725</td>\n",
       "      <td>0.280073</td>\n",
       "      <td>0.408592</td>\n",
       "      <td>0.077550</td>\n",
       "      <td>0.157891</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.010878</td>\n",
       "      <td>-0.082531</td>\n",
       "      <td>0.109687</td>\n",
       "      <td>0.025646</td>\n",
       "      <td>0.032877</td>\n",
       "      <td>0.291857</td>\n",
       "      <td>-0.038093</td>\n",
       "      <td>0.148594</td>\n",
       "      <td>0.454963</td>\n",
       "      <td>0.068445</td>\n",
       "      <td>-0.083826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pub_rec</th>\n",
       "      <td>-0.089864</td>\n",
       "      <td>0.016200</td>\n",
       "      <td>-0.017884</td>\n",
       "      <td>-0.001365</td>\n",
       "      <td>-0.056106</td>\n",
       "      <td>-0.035484</td>\n",
       "      <td>0.010878</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.051907</td>\n",
       "      <td>-0.032329</td>\n",
       "      <td>-0.000326</td>\n",
       "      <td>-0.047253</td>\n",
       "      <td>0.076978</td>\n",
       "      <td>-0.029995</td>\n",
       "      <td>-0.095073</td>\n",
       "      <td>-0.061442</td>\n",
       "      <td>0.055240</td>\n",
       "      <td>0.031018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>int_rate</th>\n",
       "      <td>0.178345</td>\n",
       "      <td>0.023258</td>\n",
       "      <td>-0.046836</td>\n",
       "      <td>-0.044420</td>\n",
       "      <td>-0.962508</td>\n",
       "      <td>-0.006573</td>\n",
       "      <td>-0.082531</td>\n",
       "      <td>0.051907</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.010561</td>\n",
       "      <td>0.122743</td>\n",
       "      <td>-0.105081</td>\n",
       "      <td>-0.109968</td>\n",
       "      <td>0.318154</td>\n",
       "      <td>-0.308901</td>\n",
       "      <td>-0.092296</td>\n",
       "      <td>0.167055</td>\n",
       "      <td>0.320303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>open_acc</th>\n",
       "      <td>0.217118</td>\n",
       "      <td>0.067730</td>\n",
       "      <td>0.164285</td>\n",
       "      <td>0.118312</td>\n",
       "      <td>-0.017717</td>\n",
       "      <td>0.070983</td>\n",
       "      <td>0.109687</td>\n",
       "      <td>-0.032329</td>\n",
       "      <td>0.010561</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.624604</td>\n",
       "      <td>-0.190479</td>\n",
       "      <td>0.135990</td>\n",
       "      <td>-0.097383</td>\n",
       "      <td>0.237500</td>\n",
       "      <td>-0.086458</td>\n",
       "      <td>0.449273</td>\n",
       "      <td>0.039068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_actv_rev_tl</th>\n",
       "      <td>0.147857</td>\n",
       "      <td>0.115884</td>\n",
       "      <td>0.058023</td>\n",
       "      <td>0.039594</td>\n",
       "      <td>-0.115849</td>\n",
       "      <td>0.000549</td>\n",
       "      <td>0.025646</td>\n",
       "      <td>-0.000326</td>\n",
       "      <td>0.122743</td>\n",
       "      <td>0.624604</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.217517</td>\n",
       "      <td>0.141621</td>\n",
       "      <td>0.112672</td>\n",
       "      <td>0.066917</td>\n",
       "      <td>-0.172794</td>\n",
       "      <td>0.269771</td>\n",
       "      <td>0.091944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mo_sin_rcnt_rev_tl_op</th>\n",
       "      <td>0.051495</td>\n",
       "      <td>0.002138</td>\n",
       "      <td>0.039310</td>\n",
       "      <td>0.019183</td>\n",
       "      <td>0.102423</td>\n",
       "      <td>0.024228</td>\n",
       "      <td>0.032877</td>\n",
       "      <td>-0.047253</td>\n",
       "      <td>-0.105081</td>\n",
       "      <td>-0.190479</td>\n",
       "      <td>-0.217517</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.051160</td>\n",
       "      <td>0.106012</td>\n",
       "      <td>-0.044681</td>\n",
       "      <td>0.158652</td>\n",
       "      <td>-0.391376</td>\n",
       "      <td>-0.069761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mo_sin_old_rev_tl_op</th>\n",
       "      <td>0.166818</td>\n",
       "      <td>0.118255</td>\n",
       "      <td>0.163209</td>\n",
       "      <td>0.103986</td>\n",
       "      <td>0.103569</td>\n",
       "      <td>0.085405</td>\n",
       "      <td>0.291857</td>\n",
       "      <td>0.076978</td>\n",
       "      <td>-0.109968</td>\n",
       "      <td>0.135990</td>\n",
       "      <td>0.141621</td>\n",
       "      <td>0.051160</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.038183</td>\n",
       "      <td>0.192015</td>\n",
       "      <td>0.137734</td>\n",
       "      <td>-0.043916</td>\n",
       "      <td>-0.054067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bc_util</th>\n",
       "      <td>0.039460</td>\n",
       "      <td>0.041380</td>\n",
       "      <td>-0.000826</td>\n",
       "      <td>0.009546</td>\n",
       "      <td>-0.290733</td>\n",
       "      <td>-0.050633</td>\n",
       "      <td>-0.038093</td>\n",
       "      <td>-0.029995</td>\n",
       "      <td>0.318154</td>\n",
       "      <td>-0.097383</td>\n",
       "      <td>0.112672</td>\n",
       "      <td>0.106012</td>\n",
       "      <td>-0.038183</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.566770</td>\n",
       "      <td>0.034228</td>\n",
       "      <td>-0.138065</td>\n",
       "      <td>0.103574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bc_open_to_buy</th>\n",
       "      <td>0.194762</td>\n",
       "      <td>0.015400</td>\n",
       "      <td>0.166425</td>\n",
       "      <td>0.076216</td>\n",
       "      <td>0.279636</td>\n",
       "      <td>0.136156</td>\n",
       "      <td>0.148594</td>\n",
       "      <td>-0.095073</td>\n",
       "      <td>-0.308901</td>\n",
       "      <td>0.237500</td>\n",
       "      <td>0.066917</td>\n",
       "      <td>-0.044681</td>\n",
       "      <td>0.192015</td>\n",
       "      <td>-0.566770</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.073307</td>\n",
       "      <td>0.055448</td>\n",
       "      <td>-0.122477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_cur_bal</th>\n",
       "      <td>0.235574</td>\n",
       "      <td>0.100102</td>\n",
       "      <td>0.392761</td>\n",
       "      <td>0.395941</td>\n",
       "      <td>0.088589</td>\n",
       "      <td>0.155880</td>\n",
       "      <td>0.454963</td>\n",
       "      <td>-0.061442</td>\n",
       "      <td>-0.092296</td>\n",
       "      <td>-0.086458</td>\n",
       "      <td>-0.172794</td>\n",
       "      <td>0.158652</td>\n",
       "      <td>0.137734</td>\n",
       "      <td>0.034228</td>\n",
       "      <td>0.073307</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.065925</td>\n",
       "      <td>-0.099991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acc_open_past_24mths</th>\n",
       "      <td>0.022169</td>\n",
       "      <td>0.036747</td>\n",
       "      <td>0.063209</td>\n",
       "      <td>0.052456</td>\n",
       "      <td>-0.171499</td>\n",
       "      <td>0.012274</td>\n",
       "      <td>0.068445</td>\n",
       "      <td>0.055240</td>\n",
       "      <td>0.167055</td>\n",
       "      <td>0.449273</td>\n",
       "      <td>0.269771</td>\n",
       "      <td>-0.391376</td>\n",
       "      <td>-0.043916</td>\n",
       "      <td>-0.138065</td>\n",
       "      <td>0.055448</td>\n",
       "      <td>-0.065925</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.110856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan_status</th>\n",
       "      <td>0.079202</td>\n",
       "      <td>-0.005707</td>\n",
       "      <td>-0.086017</td>\n",
       "      <td>-0.058254</td>\n",
       "      <td>-0.314444</td>\n",
       "      <td>-0.478829</td>\n",
       "      <td>-0.083826</td>\n",
       "      <td>0.031018</td>\n",
       "      <td>0.320303</td>\n",
       "      <td>0.039068</td>\n",
       "      <td>0.091944</td>\n",
       "      <td>-0.069761</td>\n",
       "      <td>-0.054067</td>\n",
       "      <td>0.103574</td>\n",
       "      <td>-0.122477</td>\n",
       "      <td>-0.099991</td>\n",
       "      <td>0.110856</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       funded_amnt  emp_length  annual_inc  home_ownership  \\\n",
       "funded_amnt               1.000000    0.135988    0.425629        0.187621   \n",
       "emp_length                0.135988    1.000000    0.115969        0.137079   \n",
       "annual_inc                0.425629    0.115969    1.000000        0.192284   \n",
       "home_ownership            0.187621    0.137079    0.192284        1.000000   \n",
       "grade                    -0.181152   -0.019547    0.039387        0.046116   \n",
       "last_pymnt_amnt           0.336927    0.057445    0.201918        0.126158   \n",
       "mort_acc                  0.230845    0.166725    0.280073        0.408592   \n",
       "pub_rec                  -0.089864    0.016200   -0.017884       -0.001365   \n",
       "int_rate                  0.178345    0.023258   -0.046836       -0.044420   \n",
       "open_acc                  0.217118    0.067730    0.164285        0.118312   \n",
       "num_actv_rev_tl           0.147857    0.115884    0.058023        0.039594   \n",
       "mo_sin_rcnt_rev_tl_op     0.051495    0.002138    0.039310        0.019183   \n",
       "mo_sin_old_rev_tl_op      0.166818    0.118255    0.163209        0.103986   \n",
       "bc_util                   0.039460    0.041380   -0.000826        0.009546   \n",
       "bc_open_to_buy            0.194762    0.015400    0.166425        0.076216   \n",
       "avg_cur_bal               0.235574    0.100102    0.392761        0.395941   \n",
       "acc_open_past_24mths      0.022169    0.036747    0.063209        0.052456   \n",
       "loan_status               0.079202   -0.005707   -0.086017       -0.058254   \n",
       "\n",
       "                          grade  last_pymnt_amnt  mort_acc   pub_rec  \\\n",
       "funded_amnt           -0.181152         0.336927  0.230845 -0.089864   \n",
       "emp_length            -0.019547         0.057445  0.166725  0.016200   \n",
       "annual_inc             0.039387         0.201918  0.280073 -0.017884   \n",
       "home_ownership         0.046116         0.126158  0.408592 -0.001365   \n",
       "grade                  1.000000        -0.001873  0.077550 -0.056106   \n",
       "last_pymnt_amnt       -0.001873         1.000000  0.157891 -0.035484   \n",
       "mort_acc               0.077550         0.157891  1.000000  0.010878   \n",
       "pub_rec               -0.056106        -0.035484  0.010878  1.000000   \n",
       "int_rate              -0.962508        -0.006573 -0.082531  0.051907   \n",
       "open_acc              -0.017717         0.070983  0.109687 -0.032329   \n",
       "num_actv_rev_tl       -0.115849         0.000549  0.025646 -0.000326   \n",
       "mo_sin_rcnt_rev_tl_op  0.102423         0.024228  0.032877 -0.047253   \n",
       "mo_sin_old_rev_tl_op   0.103569         0.085405  0.291857  0.076978   \n",
       "bc_util               -0.290733        -0.050633 -0.038093 -0.029995   \n",
       "bc_open_to_buy         0.279636         0.136156  0.148594 -0.095073   \n",
       "avg_cur_bal            0.088589         0.155880  0.454963 -0.061442   \n",
       "acc_open_past_24mths  -0.171499         0.012274  0.068445  0.055240   \n",
       "loan_status           -0.314444        -0.478829 -0.083826  0.031018   \n",
       "\n",
       "                       int_rate  open_acc  num_actv_rev_tl  \\\n",
       "funded_amnt            0.178345  0.217118         0.147857   \n",
       "emp_length             0.023258  0.067730         0.115884   \n",
       "annual_inc            -0.046836  0.164285         0.058023   \n",
       "home_ownership        -0.044420  0.118312         0.039594   \n",
       "grade                 -0.962508 -0.017717        -0.115849   \n",
       "last_pymnt_amnt       -0.006573  0.070983         0.000549   \n",
       "mort_acc              -0.082531  0.109687         0.025646   \n",
       "pub_rec                0.051907 -0.032329        -0.000326   \n",
       "int_rate               1.000000  0.010561         0.122743   \n",
       "open_acc               0.010561  1.000000         0.624604   \n",
       "num_actv_rev_tl        0.122743  0.624604         1.000000   \n",
       "mo_sin_rcnt_rev_tl_op -0.105081 -0.190479        -0.217517   \n",
       "mo_sin_old_rev_tl_op  -0.109968  0.135990         0.141621   \n",
       "bc_util                0.318154 -0.097383         0.112672   \n",
       "bc_open_to_buy        -0.308901  0.237500         0.066917   \n",
       "avg_cur_bal           -0.092296 -0.086458        -0.172794   \n",
       "acc_open_past_24mths   0.167055  0.449273         0.269771   \n",
       "loan_status            0.320303  0.039068         0.091944   \n",
       "\n",
       "                       mo_sin_rcnt_rev_tl_op  mo_sin_old_rev_tl_op   bc_util  \\\n",
       "funded_amnt                         0.051495              0.166818  0.039460   \n",
       "emp_length                          0.002138              0.118255  0.041380   \n",
       "annual_inc                          0.039310              0.163209 -0.000826   \n",
       "home_ownership                      0.019183              0.103986  0.009546   \n",
       "grade                               0.102423              0.103569 -0.290733   \n",
       "last_pymnt_amnt                     0.024228              0.085405 -0.050633   \n",
       "mort_acc                            0.032877              0.291857 -0.038093   \n",
       "pub_rec                            -0.047253              0.076978 -0.029995   \n",
       "int_rate                           -0.105081             -0.109968  0.318154   \n",
       "open_acc                           -0.190479              0.135990 -0.097383   \n",
       "num_actv_rev_tl                    -0.217517              0.141621  0.112672   \n",
       "mo_sin_rcnt_rev_tl_op               1.000000              0.051160  0.106012   \n",
       "mo_sin_old_rev_tl_op                0.051160              1.000000 -0.038183   \n",
       "bc_util                             0.106012             -0.038183  1.000000   \n",
       "bc_open_to_buy                     -0.044681              0.192015 -0.566770   \n",
       "avg_cur_bal                         0.158652              0.137734  0.034228   \n",
       "acc_open_past_24mths               -0.391376             -0.043916 -0.138065   \n",
       "loan_status                        -0.069761             -0.054067  0.103574   \n",
       "\n",
       "                       bc_open_to_buy  avg_cur_bal  acc_open_past_24mths  \\\n",
       "funded_amnt                  0.194762     0.235574              0.022169   \n",
       "emp_length                   0.015400     0.100102              0.036747   \n",
       "annual_inc                   0.166425     0.392761              0.063209   \n",
       "home_ownership               0.076216     0.395941              0.052456   \n",
       "grade                        0.279636     0.088589             -0.171499   \n",
       "last_pymnt_amnt              0.136156     0.155880              0.012274   \n",
       "mort_acc                     0.148594     0.454963              0.068445   \n",
       "pub_rec                     -0.095073    -0.061442              0.055240   \n",
       "int_rate                    -0.308901    -0.092296              0.167055   \n",
       "open_acc                     0.237500    -0.086458              0.449273   \n",
       "num_actv_rev_tl              0.066917    -0.172794              0.269771   \n",
       "mo_sin_rcnt_rev_tl_op       -0.044681     0.158652             -0.391376   \n",
       "mo_sin_old_rev_tl_op         0.192015     0.137734             -0.043916   \n",
       "bc_util                     -0.566770     0.034228             -0.138065   \n",
       "bc_open_to_buy               1.000000     0.073307              0.055448   \n",
       "avg_cur_bal                  0.073307     1.000000             -0.065925   \n",
       "acc_open_past_24mths         0.055448    -0.065925              1.000000   \n",
       "loan_status                 -0.122477    -0.099991              0.110856   \n",
       "\n",
       "                       loan_status  \n",
       "funded_amnt               0.079202  \n",
       "emp_length               -0.005707  \n",
       "annual_inc               -0.086017  \n",
       "home_ownership           -0.058254  \n",
       "grade                    -0.314444  \n",
       "last_pymnt_amnt          -0.478829  \n",
       "mort_acc                 -0.083826  \n",
       "pub_rec                   0.031018  \n",
       "int_rate                  0.320303  \n",
       "open_acc                  0.039068  \n",
       "num_actv_rev_tl           0.091944  \n",
       "mo_sin_rcnt_rev_tl_op    -0.069761  \n",
       "mo_sin_old_rev_tl_op     -0.054067  \n",
       "bc_util                   0.103574  \n",
       "bc_open_to_buy           -0.122477  \n",
       "avg_cur_bal              -0.099991  \n",
       "acc_open_past_24mths      0.110856  \n",
       "loan_status               1.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_clean.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Learning Curve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "<span style=\"color:blue\">\n",
    "> This learning curve clearly shows that our models are not learning anything after ~11,000 samples. So we randomly sampled our dataset and used only 11,000 samples of our dataset.<br>\n",
    "> Note: In the plot, only ~9000 samples are shown because it is a plot for training set.\n",
    "</span>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x129343278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import learning_curve\n",
    "from sklearn.model_selection import ShuffleSplit\n",
    "\n",
    "def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\n",
    "                        n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):\n",
    "    plt.figure()\n",
    "    plt.title(title)\n",
    "    if ylim is not None:\n",
    "        plt.ylim(*ylim)\n",
    "    plt.xlabel(\"Training examples\")\n",
    "    plt.ylabel(\"Score\")\n",
    "    train_sizes, train_scores, test_scores = learning_curve(\n",
    "        estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    train_scores_std = np.std(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "    test_scores_std = np.std(test_scores, axis=1)\n",
    "    plt.grid()\n",
    "    \n",
    "    plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
    "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
    "                     color=\"r\")\n",
    "    plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
    "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n",
    "    plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n",
    "             label=\"Training score\")\n",
    "    plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n",
    "             label=\"Cross-validation score\")\n",
    "    plt.legend(loc=\"best\")\n",
    "    return plt\n",
    "\n",
    "X, y = data_clean.iloc[:,:-1].values, data_clean.iloc[:,-1].values\n",
    "title = \"Learning Curves (Logistic Regression)\"\n",
    "# Cross validation with 100 iterations to get smoother mean test and train\n",
    "# score curves, each time with 20% data randomly selected as a validation set.\n",
    "cv = ShuffleSplit(n_splits=100, test_size=0.2, random_state=0)\n",
    "estimator = linear_model.LogisticRegression()\n",
    "plot_learning_curve(estimator, title, X, y, ylim=(0.75, 0.90), cv=cv, n_jobs=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ROC Curve plot function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "<span style=\"color:blue\">\n",
    "This is a callable ROC curve plot function. We have used this function to plot ROC Curve for all the models. We have used Seaborn package.\n",
    "</span>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "sns.set('talk', 'whitegrid', 'dark', font_scale=1, font='Ricty',rc={\"lines.linewidth\": 2, 'grid.linestyle': '--'})\n",
    "def plotAUC(truth, pred, lab):\n",
    "    fpr, tpr, _ = metrics.roc_curve(truth,pred)\n",
    "    roc_auc = metrics.auc(fpr, tpr)\n",
    "    lw = 2\n",
    "    c = (np.random.rand(), np.random.rand(), np.random.rand())\n",
    "    plt.plot(fpr, tpr, color= c,lw=lw, label= lab +'(AUC = %0.2f)' % roc_auc)\n",
    "    plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.0])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('ROC curve') #Receiver Operating Characteristic \n",
    "    plt.legend(loc=\"lower right\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confusion Matrix Viz function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "<span style=\"color:blue\">\n",
    "This is a callable Confusion Matrix Visualization function. We have used this function to visualize True positives, True Negatives, False Positives and False Negatives for all the models.\n",
    "</span>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "from sklearn.metrics import confusion_matrix\n",
    "def plot_confusion_matrix(model, normalize=False): # This function prints and plots the confusion matrix.\n",
    "    cm = confusion_matrix(y_test, model, labels=[0, 1])\n",
    "    classes=[\"Will Pay\", \"Will Default\"]\n",
    "    cmap = plt.cm.Blues\n",
    "    title = \"Confusion Matrix\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        cm = np.around(cm, decimals=3)\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, cm[i, j],\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(data_clean.iloc[:,:-1], data_clean.iloc[:,-1], test_size=0.2, random_state=42)\n",
    "bs_train, bs_test = train_test_split(data_clean, test_size = 0.2, random_state=42) #just for bootstrapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>grade</th>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <th>mort_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>num_actv_rev_tl</th>\n",
       "      <th>mo_sin_rcnt_rev_tl_op</th>\n",
       "      <th>mo_sin_old_rev_tl_op</th>\n",
       "      <th>bc_util</th>\n",
       "      <th>bc_open_to_buy</th>\n",
       "      <th>avg_cur_bal</th>\n",
       "      <th>acc_open_past_24mths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10735</th>\n",
       "      <td>-0.597817</td>\n",
       "      <td>0.043547</td>\n",
       "      <td>-0.150887</td>\n",
       "      <td>-0.634134</td>\n",
       "      <td>-0.950935</td>\n",
       "      <td>-0.666323</td>\n",
       "      <td>-0.840272</td>\n",
       "      <td>-0.323888</td>\n",
       "      <td>0.856914</td>\n",
       "      <td>-0.263079</td>\n",
       "      <td>-0.584087</td>\n",
       "      <td>-0.607851</td>\n",
       "      <td>-0.433740</td>\n",
       "      <td>-0.128023</td>\n",
       "      <td>-0.446164</td>\n",
       "      <td>-0.825479</td>\n",
       "      <td>1.361184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5937</th>\n",
       "      <td>0.341719</td>\n",
       "      <td>1.140751</td>\n",
       "      <td>-0.650588</td>\n",
       "      <td>0.899053</td>\n",
       "      <td>-0.179927</td>\n",
       "      <td>-0.649311</td>\n",
       "      <td>0.087642</td>\n",
       "      <td>-0.323888</td>\n",
       "      <td>0.788309</td>\n",
       "      <td>0.341840</td>\n",
       "      <td>0.792362</td>\n",
       "      <td>0.246488</td>\n",
       "      <td>-1.282396</td>\n",
       "      <td>0.324497</td>\n",
       "      <td>-0.243299</td>\n",
       "      <td>-0.233739</td>\n",
       "      <td>0.638473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7642</th>\n",
       "      <td>-0.018234</td>\n",
       "      <td>0.317848</td>\n",
       "      <td>0.959561</td>\n",
       "      <td>-2.167321</td>\n",
       "      <td>1.362088</td>\n",
       "      <td>-0.676423</td>\n",
       "      <td>1.015556</td>\n",
       "      <td>-0.323888</td>\n",
       "      <td>-1.402494</td>\n",
       "      <td>-0.666359</td>\n",
       "      <td>0.104137</td>\n",
       "      <td>0.903672</td>\n",
       "      <td>0.667220</td>\n",
       "      <td>0.006199</td>\n",
       "      <td>-0.162930</td>\n",
       "      <td>0.215017</td>\n",
       "      <td>-1.168306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3328</th>\n",
       "      <td>-0.643574</td>\n",
       "      <td>-1.602258</td>\n",
       "      <td>-0.918947</td>\n",
       "      <td>-0.634134</td>\n",
       "      <td>-0.179927</td>\n",
       "      <td>-0.698461</td>\n",
       "      <td>-0.840272</td>\n",
       "      <td>-0.323888</td>\n",
       "      <td>-0.190464</td>\n",
       "      <td>1.551678</td>\n",
       "      <td>-0.584087</td>\n",
       "      <td>0.180770</td>\n",
       "      <td>-0.708980</td>\n",
       "      <td>-0.756950</td>\n",
       "      <td>0.772092</td>\n",
       "      <td>-0.500871</td>\n",
       "      <td>-0.084239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8681</th>\n",
       "      <td>-0.262269</td>\n",
       "      <td>0.043547</td>\n",
       "      <td>-0.613573</td>\n",
       "      <td>0.899053</td>\n",
       "      <td>-0.950935</td>\n",
       "      <td>-0.678230</td>\n",
       "      <td>-0.840272</td>\n",
       "      <td>-0.323888</td>\n",
       "      <td>0.632803</td>\n",
       "      <td>-0.666359</td>\n",
       "      <td>-0.239975</td>\n",
       "      <td>-0.673570</td>\n",
       "      <td>0.254360</td>\n",
       "      <td>-0.312099</td>\n",
       "      <td>-0.038682</td>\n",
       "      <td>-0.475733</td>\n",
       "      <td>0.638473</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       funded_amnt  emp_length  annual_inc  home_ownership     grade  \\\n",
       "10735    -0.597817    0.043547   -0.150887       -0.634134 -0.950935   \n",
       "5937      0.341719    1.140751   -0.650588        0.899053 -0.179927   \n",
       "7642     -0.018234    0.317848    0.959561       -2.167321  1.362088   \n",
       "3328     -0.643574   -1.602258   -0.918947       -0.634134 -0.179927   \n",
       "8681     -0.262269    0.043547   -0.613573        0.899053 -0.950935   \n",
       "\n",
       "       last_pymnt_amnt  mort_acc   pub_rec  int_rate  open_acc  \\\n",
       "10735        -0.666323 -0.840272 -0.323888  0.856914 -0.263079   \n",
       "5937         -0.649311  0.087642 -0.323888  0.788309  0.341840   \n",
       "7642         -0.676423  1.015556 -0.323888 -1.402494 -0.666359   \n",
       "3328         -0.698461 -0.840272 -0.323888 -0.190464  1.551678   \n",
       "8681         -0.678230 -0.840272 -0.323888  0.632803 -0.666359   \n",
       "\n",
       "       num_actv_rev_tl  mo_sin_rcnt_rev_tl_op  mo_sin_old_rev_tl_op   bc_util  \\\n",
       "10735        -0.584087              -0.607851             -0.433740 -0.128023   \n",
       "5937          0.792362               0.246488             -1.282396  0.324497   \n",
       "7642          0.104137               0.903672              0.667220  0.006199   \n",
       "3328         -0.584087               0.180770             -0.708980 -0.756950   \n",
       "8681         -0.239975              -0.673570              0.254360 -0.312099   \n",
       "\n",
       "       bc_open_to_buy  avg_cur_bal  acc_open_past_24mths  \n",
       "10735       -0.446164    -0.825479              1.361184  \n",
       "5937        -0.243299    -0.233739              0.638473  \n",
       "7642        -0.162930     0.215017             -1.168306  \n",
       "3328         0.772092    -0.500871             -0.084239  \n",
       "8681        -0.038682    -0.475733              0.638473  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Feature Selection using RFE (Recursive Feature Elimination) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True False  True False  True  True  True  True  True False False False\n",
      " False  True  True False  True]\n",
      "[1 3 1 7 1 1 1 1 1 8 4 6 5 1 1 2 1]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import RFE\n",
    "# create the RFE model and select 3 attributes\n",
    "clf_LR = linear_model.LogisticRegression(C=1e30)\n",
    "clf_LR.fit(X_train,y_train)\n",
    "rfe = RFE(clf_LR, 10)\n",
    "rfe = rfe.fit(data_clean.iloc[:,:-1].values, data_clean.iloc[:,-1].values)\n",
    "# summarize the selection of the attributes\n",
    "print(rfe.support_)\n",
    "print(rfe.ranking_)\n",
    "# ['funded_amnt','emp_length','annual_inc','home_ownership','grade',\"last_pymnt_amnt\", \"mort_acc\", \"pub_rec\", \n",
    "# \"int_rate\", \"open_acc\",\"num_actv_rev_tl\",\"mo_sin_rcnt_rev_tl_op\",\"mo_sin_old_rev_tl_op\",\"bc_util\",\"bc_open_to_buy\",\n",
    "#\"avg_cur_bal\",\"acc_open_past_24mths\",'loan_status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Expected Variance is [ 0.16480406  0.15995948  0.126555    0.07380367  0.06972489  0.06155867\n",
      "  0.05734383  0.04691656  0.0440577   0.03924073]\n"
     ]
    }
   ],
   "source": [
    "#PCA (Principal Component Analysis)\n",
    "from sklearn.decomposition import PCA \n",
    "pca = PCA(n_components=10, whiten=True)\n",
    "X_train_pca = pca.fit_transform(X_train)\n",
    "X_test_pca = pca.transform(X_test)\n",
    "explained_variance = pca.explained_variance_ratio_\n",
    "print('Expected Variance is '+ str(explained_variance))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8800, 10)\n",
      "(11000, 11)\n"
     ]
    }
   ],
   "source": [
    "features = ['funded_amnt','annual_inc','grade',\"last_pymnt_amnt\", \"int_rate\",\n",
    "            \"mo_sin_rcnt_rev_tl_op\",\"mo_sin_old_rev_tl_op\",\"bc_util\",\"bc_open_to_buy\",\"acc_open_past_24mths\",\"loan_status\"]\n",
    "X_train, X_test = X_train[features[:-1]], X_test[features[:-1]]\n",
    "data_clean = data_clean[features]\n",
    "print(X_train.shape)\n",
    "print(data_clean.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1298e3ef0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1298e3780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataViz = data_clean\n",
    "sns.set_context(context='notebook')\n",
    "fig, ax = plt.subplots(figsize=(10,10)) \n",
    "corr = dataViz.corr()\n",
    "\n",
    "# Generate a mask for the upper triangle\n",
    "mask = np.zeros_like(corr, dtype=np.bool)\n",
    "mask[np.tril_indices_from(mask)] = True\n",
    "\n",
    "# Generate a custom diverging colormap\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "\n",
    "sns.heatmap(corr, cmap=cmap,linewidths=1, vmin=-1, vmax=1, square=True, cbar=True, center=0, ax=ax, mask=mask)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Models\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest with randomized Search"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "<span style=\"color:blue\">\n",
    "Random forest when implemented with randomized search we got the best accuracies and minimum false negatives(predicting borowwer will not default eventhough he will. This might impact on the credibility of the company). We used the randomized search to find the best hyper paramters for the model.\n",
    "</span>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "            max_depth=None, max_features=6, max_leaf_nodes=None,\n",
      "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "            n_estimators=10, n_jobs=1, oob_score=False, random_state=0,\n",
      "            verbose=0, warm_start=False)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.grid_search import RandomizedSearchCV\n",
    "rf = RandomForestClassifier(criterion='gini', random_state=0)\n",
    "maxFeatures = range(1,data_clean.shape[1]-1)\n",
    "param_dist = dict(max_features=maxFeatures)\n",
    "rand = RandomizedSearchCV(rf, param_dist, cv=10, scoring='accuracy', n_iter=len(maxFeatures), random_state=10)\n",
    "X=data_clean.iloc[:,:-1].values\n",
    "y=data_clean.iloc[:,-1].values\n",
    "rand.fit(X,y)\n",
    "mean_scores = [result.mean_validation_score for result in rand.grid_scores_]\n",
    "#print('Best Accuracy = '+str(rand.best_score_))\n",
    "print(rand.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.820909090909\n"
     ]
    }
   ],
   "source": [
    "randomForest = RandomForestClassifier(bootstrap=True,criterion = \"gini\",max_features=rand.best_estimator_.max_features,random_state=0 )\n",
    "randomForest.fit(X_train,y_train)\n",
    "rfPredict = randomForest.predict(X_test)\n",
    "rfPredictproba = randomForest.predict_proba(X_test)[:,1] #for ROC curve\n",
    "rfAccuracy = accuracy_score(y_test,rfPredict)\n",
    "roc_score = metrics.roc_auc_score(y_test,rfPredict)\n",
    "print(rfAccuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x12a4b6a58>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bcd1ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "width=0.35\n",
    "ax.bar(np.arange(len(features)-1), randomForest.feature_importances_, width, color='r')\n",
    "ax.set_xticks(np.arange(len(randomForest.feature_importances_)))\n",
    "ax.set_xticklabels(X_train.columns.values,rotation=90)\n",
    "plt.title('Feature Importance from DT')\n",
    "ax.set_ylabel('Normalized Gini Importance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SsfpsOePc3BL69l3OwYOZGAzw5JM3MHfuIDw97WeQIU1eWF36pkziZxzBlGfCM9yDDvNa0+SmAJs/LCaEsC0+Ph5ERwdTXm5hxYrR3HBDuNYlNThp8sJqLOUWji44yamlpwEIHhZI9JvtHOLQvBBCH+LiUnF1dSY6uuIRsO+/PwI3N2e7Gr1fSpq8uEy7du0afJ0laaXsm3aQ7N9zMbgYaPtsK1o+GO7Qo3c1chZVScbqs5WMy8rMzJ8fyyuv/EyHDkHs2fMQbm7O+Ph4aF2aqqTJC9Vl7bjAH48coux8OR4h7nR9vwP+PX21LksI4SD27k1j/Pg1JCScw2CAIUNaYjZbAP09UKahyX3y4jJHjhxpkPUoFoXjb51i9737KTtfTuAAP/pu7iEN/k8NlbOonmSsPj1nXFZmZs6cH7n++hgSEs4RGelHbOx43nrrb3Z7eP5/yUheqKL0fBn7px8iKzYbDNB6Rgui/tkCg7PjHp4XQliPoigMHfox27cnAzB9ei9efnkIjRq5aVyZdUmTFw0ue08u+6YepCS9FDd/V7q814GgAf5alyWEcCAGg4EHHojmzJk8li8fxcCBLbQuSRPS5MVlQkPr93QlRVFIijnDkZcSUUwKfj286bq4I55h9n1hS33VN2dRe5Kx+vSUcXx8BocPZ3L33Z0AmDixG2PHdsZodIxD81ciTV5cpj4TW5TnmYifeYSMjZkAtJwSTttZrXBylcs+qmOrE4jYEslYfXrIuLzczIIFPzN//nZcXJy47rpQWreumHvDkRs8yIV34gri4+PrtHzewXx+Gb6HjI2ZuDR25roPO9H+X1HS4GtQ15xF3UnG6tM64wMHztG79zJmz/6J8nIL48d3JTS0saY16YmM5MVlysvLa7Wcoiic+TyNg88fx1JqwbujF92WdqJRC0+VK7QPtc1Z1J9krD6tMjaZLLz22i+88EIsZWVmIiJ8WLZsFEOGtNKkHr2SJi/qxVxs5sCzxzj7n3QAwu8NpcPc1jh72v99p0II7U2btoEPP9wLwJQp3XnttaE0bmx/j6S+VtLkxWWMRuNVv16QWMTehw5QcLQQJw8nOi1oS7M7Q6xUnf2oKWdx7SRj9WmV8WOP9eann5JYtGg4Q4dGalKDLTAoiqJoXURdxMXF0b17d63LcFhp354j/okjmAvNNIo0ct3SjjRu56V1WUIIO3f4cCYffxzPSy8NrpwO22y24OzsGNf+1Lf3OUY6ok6Sk5Mve89SZuHgv46x7+GDmAvNhI5uQt8N3aXBX4Mr5SwalmSsPrUzNpsrzr1367aUV175mc8/P1D5NUdp8NdCEhKXycrKqvK6+EwJv96+l+QVZzG4GujwYmu6vtcBFy8523Mt/jdn0fAkY/WpmfHRo1n077+Cp576ntJSMxMndmX48Naqbc8eyW9pcVXntp1n/z8PUZ5jwrOZB92WdMS3q7fWZQkh7JjZbOHf/97Fc8/9QEmJibCwxnz44a3S4OtBmryoVtr6c+x7+CAo0OSmAKLfao+bn2NPLCGEUN+SJXuYOXMLAOPGdeGtt4bh5ye35taHXHgnLlNWVkbBH0XsHrsfS6mFqH9G0HpmSwxO8nCZhlRWVoabm2M9LMPaJGP1qZFxSYmJkSM/47HHejNyZJsGXbetkgvvRIPJOnCBuAkJWEotNB/XlNZPSINXQ1FRkdYl2D3JWH0NkfGJExe4++6vyMkpAcDDw4Xvv39AGnwDkCYvqig9V0r85COU55poMjSADnOjKm9XEQ0rMTFR6xLsnmSsvmvJ2GJRePfdXURHv8+XXx5kzpwfG7AyAXJOXlzCVGTm9/EJWM4p+HRpTNdFHXFykb8DhRAN7+TJbCZOXEtsbMUteH//e2fmzBmkbVF2SJq8AMBisrDv4YPkxefjHGygx6poXIwyRa0QomFZLArvv/87Tz31PUVF5TRp0oilS0cyZkw7rUuzS9LkBYqicPD542RuO4+rrwttl7TEPVAuVlJb8+bNtS7B7knG6qtrxrt2neGRRzYCMHZsJ9599xYCAmT6YbVIkxecXJTC6U9ScXJ3ovuKzvj39NW6JIcQFBSkdQl2TzJWX10z7tMnnKef7kuvXk25/fb2KlUl/iInXB3c2f9mcHTBSTBAl3fa49/Tl7i4OK3LcgiSs/okY/XVlHFycg7Dhn3Czp2nK99bsOAmafBWIiN5B3b+l2ziZxwGoP2cKEJHNNG4IiGEvVAUhQ8/3MvMmVsoKCijsLCMn3+eqHVZDkeavIPKP1JA3IMHUMoVWkxqRsvJ4VqXJISwEykpuUyevI6tW08CcOedHVi0aLjGVTkmafIOqCS9lN8fiMeUZyL4liDaz46q8nUfHx+NKnMskrP6JGP1XZqxoigsW7aPGTM2k59fRkCAJ4sXj+D//q+jhhU6NlXPyW/fvp1hw4YxdOhQPvjgg8u+np+fz9SpUxk1ahQjRozg66+/VrMcAZgKTOwZF09Jaim+3b3p+k57DM5VJ7uJioqq5rtFQ5Kc1ScZq+/SjC9cKObpp78nP7+M225rx8GD06TBa0y1Jm82m5k3bx4xMTFs2LCB9evXc+LEiSrLfPrpp0RGRrJu3To+/vhjXn31VcrKytQqyeFZyi3snXKAvIMFGFt60mNFZ5w9L78X/n9/TkIdkrP6JGP1HT9+HLPZAkBAgJEPP7yVzz67na+//j+Cg700rk6o1uTj4+OJiIggPDwcNzc3RowYwbZt26osYzAYKCwsRFEUCgsL8fHxwcVFziCoQVEUDsw6RlZsNm4BrvT8uAtu/le+Fz43N9fK1TkmyVl9krG6zp7NY8KE73n11V8q37v99vaMHdtZpsPWCdWafEZGBiEhIZWvg4ODycjIqLLM3//+dxITE+nfvz+jRo3iueeew8lJ7upTw4m3kzjzRRpOHk70WNGZRi3ksY1CiPpRFIVVq/6gY8fF/PLLOd55ZxeFhXIUVo80HTb//PPPtG/fno8++oiUlBQmTJhAjx498PK6+iGeS+/LbNeuYirEI0eOVL4XGhpKWFgY8fHxlJeXA2A0Gmnfvj3JyclkZWVVLtu5c2eKioqqPGShefPmBAUFVdmOj48PUVFRnDhxosrooHv37mRmZpKSklL5XmRkJEajkYSEhMr3AgMDiYiI4PDhw5VPbXJ1dSU6OprU1FTS0tJU+0zF28rIe6cEnKDxDHcSlRMQV/1nAnT/mezh5wSQk5NjV59Jjz+nwsJCu/tMWv6cMjNLeO+9ZDZsOA5A//7BPPdcZ8rLiygrwyY/ky39nOpKtefJ79u3j/fee49ly5YBsHTpUgCmTJlSucxDDz3EQw89RI8ePQB44IEHeOKJJ4iOjq52vfI8+brJjL3AnnHxKCaFDi+2psX4ZlqXJISwQYqi8Mkn8UyfvomcnBJ8fT3497//xv33R8uheSvQ3fPkO3fuTFJSEqdPn6asrIwNGzYwePDgKsuEhoby66+/ApCVlcWpU6do1kyaUEPJO1TAvikHUEwKraaG17rBZ2ZmqlyZAMnZGiTjhqMosHz5H+TklDB8eGsOHHiYBx7octnRKaEvqh2ud3FxYfbs2UyePBmz2cwdd9xB69atWb16NQBjx45l2rRpPPPMM9x6660oisITTzyBv7+/WiU5lOLUEn6/fz+mAjOho5rQ9tnIWn9vSkqKzPltBZKz+iTja6MoCkVF5TRq5IaTk4Fly0axfXsy48Z1qRy9S8b6puo5+YEDBzJw4MAq740dO7by/4ODg1m+fLmaJTik8txyfr8/ntKMMvyv9yH6zXYYnORwmhCi9jIyCpg6dQP5+aVs3Xo/BoOBVq38aNXKT+vSRB3I/Wp2xlJmYe9DByg4WkijKCPXxXTG2UOeCy+EqB1FUfj88wM88shGLlwoxtvbnaNHz9OuXaDWpYl6kCZvRxRFIeHJI5z/JQe3IDd6fhyNm59rndcTGVn7Q/ui/iRn9UnGdXPuXCEPP7yBb76peHDVzTdHEhNzK+Hh1U8PLBnrmzR5O3LstVOc/ToDZ6MzPVdFYwyv373wRqOxgSsTVyI5q08yrr2vvjrEww9vICuriMaN3XjjjZuZPPm6Gq+cl4z1TWaesRPnf8km8Z1kDM4Gur3fEZ/oxvVe16X3hAr1SM7qk4xr79ChTLKyirjpplYcODCNBx/sXqtb4yRjfZORvB2wlFs4OLtiYoqoxyJoMiRA44qEELYgLS2f0NCKAcEzz/SjTZsA7r67o9z3bkdkJG8HkledpeBoIcYID1o93FzrcoQQOpeVVcQ993xFdPQSzp2rmBHQ1dWZe+7pJA3ezkiTt3GlWWUcfzMJgPZzWjfIlfSBgXIVrTVIzuqTjC/33/8epmPHxXzxxUGKi8vZty+t5m+6CslY3+RwvY07+spJTHkmggb502Rowxymj4iIaJD1iKuTnNUnGV90/nwRjz66kdWrDwAwcGAEy5ePvub73iVjfZORvA3L2ZfHmS/SMLgaaD+vdYMdZjt8+HCDrEdcneSsPsm4wpYtiXTsuJjVqw9gNLry7ru38MMP4xpkYhvJWN9kJG+jFIvCweePAdBycjherRruNpa/nqwk1CU5q08yruDp6cK5c4X079+cFStGExnZcNOHS8b6Jk3eRp35Mp3c/fm4B7sR9U85XCaEqCohIYPOnYMB6N8/gp9+Gk+/fs1xkimuHYocrrdB5bnlHH2l4vnC7Z6LxMWrYf9Wc3Wt+yx5ou4kZ/U5YsbZ2cWMG7eG6OglfP/9ycr3BwyIUKXBO2LGtkRG8jbo+JtJlJ0vx6+XD2G3BTf4+qOjoxt8neJykrP6HC3j7747zoMPfktqaj4eHi6cPp2r+jYdLWNbIyN5G5N/pIDklWfBCTrOb7iL7S6Vmpra4OsUl5Oc1ecoGefklDBhwlpGjPiM1NR8brghnP37pzJhQjfVt+0oGdsqafI2RFEUDs0+jmJWaH5fGN4d6z917dWkpV3bfbOidiRn9TlCxnv3ptGp02JWrvwDd3dnXn99KNu3j6dNG+vMfOkIGdsyOVxvQ9I3ZHJ+Zw6uvi60ebKV1uUIIXSgRQtfzGaF3r2bsWLFaHkkrKhCmryNMBWZOTzvBABtnm5Vr0fICiHsw08/JdGnTzPc3V3w9/dk+/bxtGrlh7OzHJwVVckeYSNOLkqmJLUU705eNL83TNVttWvXTtX1iwqSs/rsLeO8vFIeeuhbbrxxFfPnb698v3XrAM0avL1lbG9kJG8DipKLObnkNAAd5rfG4Cz3uQrhaL7//iSTJq0jJSUXNzdnfH09tC5J2AAZyduAQ3NPYCm1EHZ7MP49fVXf3pEjR1TfhpCcrcEeMs7PL2Xq1PUMHfoxKSm59OgRxt69D/HEEzdoXRpgHxnbMxnJ61zmj+c5tyUL50bOtHsuUutyhBBWlJaWT58+y0hOzsXV1YkXXhjEU0/1xcVFxmeidqTJ65ilzMKhOccBaP1YCzyC3TWuSAhhTSEhXnToEERAgJGVK0dXTlMrRG1Jk9exUzGnKTxZTKNIIy0mNbPadkNDQ622LUcmOavPFjOOjU0iNLQxbdoEYDAY+PTT2/HycsPV1Vnr0q7IFjN2JHLMR6dK0ks58e9kADrMjcLJzXo/qrAwda/eFxUkZ/XZUsaFhWU8+uh3DBq0ivHj12A2WwDw8/PUbYMH28rYEUmT16kjLyViLjQTPCyQoEHWmbnqL/Hx8VbdnqOSnNVnKxlv355MdPQS3nvvd1xcnBg2LBKLRdG6rFqxlYwdlRyu16ELu3NI/W8GTu5OtJ8dZfXtl5eXW32bjkhyVp/eMy4qKufZZ7fxzju7UBSIjg5m1aoxdO0aonVptab3jB2dNHmdUcwKh/5VcbFdq6nhGCM8Na5ICKEGk8lC794xJCScw9nZwLPP9uf55wfg5qbfQ/PC9kiT15mUT1PJO1iAR1N3Ih+J0KQGo9GoyXYdjeSsPj1n7OLixLhxXVi5cj8rV46me3fbPLet54wFGBRFsY0TP3+Ki4uje/fuWpehirLscmL7/0Z5jom+WTFZAAAgAElEQVRuSzoSOrKJ1iUJIRrQzp2nOXeukDFjKqaCNZstmEwW3N1lvCWurr69Ty6805FjC09SnmMioK8vISOCNKsjOTlZs207EslZfXrJuLi4nCee2EK/fssZP34NZ8/mAeDs7GTzDV4vGYsrkyavE7kH8kn5JBWDs4EO89pgMGg3P31WVpZm23YkkrP69JDxb7+doVu3pbzxxq8YDAamTetJYKD9HOLWQ8aierb9J6SdUJQ/L7ZTIGJCUxq3baR1SUKIa1RSYmLOnB95/fVfsVgU2rcPZOXKMfTq1VTr0oQDkSavA6nfZJD9ey5uga60ntFS63KEEA3gvvu+4euvD+PkZODpp/vywguD8PCQX7nCumSP05ipwMSRlxMBaPtMJK7e2v9IOnfurHUJDkFyVp+WGT/9dF+OHMkiJmYUvXtbb1pqa5P9WN/knLzGTrydRGlGGb7dvGl2lz4mwCgqKtK6BIcgOavPmhnv2ZPKCy/8VPm6Z8+mxMc/bNcNHmQ/1rtaNfnCwkJ5ZrAKChKLOLXsDBigw/zWGJy0u9juUomJiVqX4BAkZ/VZI+PSUhPPP/8DvXvHMHduLJs2naj8mpNO/k2rSfZjfauxyW/fvp3hw4czbdo0oGKe4qlTp6pemL1TFIVDs4+jlCs0uzsU367eWpckhKijvXvT6NHjQ156aQcWi8LMmX0YOFCbSayEuJIam/w777zDl19+ibd3RROKjo7m9OnTqhdm785tySIr9gIu3i60ndVK63KEEHVQVmZm9uwf6dXrQw4cOEdUlD87dkzg9ddvxtPTVevyhKhUq6u8goODq7x2dZWd+FooisLRhacAaDOzJe6BbhpXVFXz5s21LsEhSM7qUyvjBQt+Zv787RgM8Nhj1/PSS0MwGh3z96Lsx/pWY5P39PTkwoULlZOz7Nmzh8aNG6temD3L/OE8BUcL8QhxJ/w+/c1XHRSk3Wx7jkRyVp9aGT/2WG9iY5OZM2cgAwY49uF52Y/1rcbD9TNmzGDSpEmcOXOG8ePH89hjj/Hkk09aoza7lbgoBYAWDzbD2V1/NzjExcVpXYJDkJzV11AZ79+fzp13fklRUcVjVb293dm27QGHb/Ag+7He1TiS79atGytXriQuLg5FUbjuuuvw8/OzRm12KXtPLtm7c3HxcaH53/U3ihdCXFRebmbBgp+ZN287JpOFTp2a8MILg7QuS4haq3EYuWDBAnx8fBg8eDBDhgzBz8+PBQsWWKM2u5S4uGIUH/FAU1y8tJ/4RghxZQkJGfTuvYzZs3/CZLIwbVoPnnjiBq3LEqJOauwyu3btuuy93377TZVi7F3+sULObcnCyd2JFhP1O0GGj4+P1iU4BMlZffXJ2GSy8OqrPzN3bizl5RYiInxYvnw0gwfLlNNXIvuxvlXb5Ddv3szmzZtJTU1l5syZle/n5+fj4eFhleLszcklFaP4Zv8XgnuQvq6ov1RUVJTWJTgEyVl99cl4y5ZEnn/+RwCmTu3OwoVDadzYvaFLsxuyH+tbtU0+PDycPn36sG/fPnr37l35vpeXF3379rVKcfakOLWE1P9mgBO0nKLvW05OnDgh/3CtQHJWX20zVhSl8g6i4cNb889/Xs/IkW246SaZw6Imsh/rW7VNvkOHDnTo0IEhQ4bg7+9vzZrsUlLMGZRyhdBbm9CohafW5VxVbm6u1iU4BMlZfbXJ+NChTKZMWc+iRcOJjq6YE+Ttt/+mdml2Q/ZjfavxnLyPjw9fffUVR44cobS0tPL9+fPnq1qYPSnPKSfl01QAWk3T9yheCEdhMll4442dzJ79E2VlZp5//gfWrRurdVlCNKgar66fM2cOv/76K1u3biUkJIS4uDicnPR3b7eeJX90FnOhmcABfvh0lomEhNDakSNZ9Ou3nFmztlFWZmbSpG58/PFtWpclRIOrsVvv37+f119/HW9vb/7xj3+wevVqTp48aY3a7IK52EzSsjOA7Yziu3fvrnUJDkFyVt//Zmw2W3jttV/o2nUJu3adpWnTxnz33b3ExIzCx0cuKK4P2Y/1rcYm7+bmhsFgwNnZmZKSEnx8fDh//rw1arMLZ75Mp+x8Od7RjQnoaxuTCGVmZmpdgkOQnNX3vxmnpuYzd24spaVmJkzoyoED07jlltYaVWcfZD/WtxrPyfv6+pKXl0ffvn2ZMmUKfn5+tZ6rePv27bz00ktYLBbuuusuHnroocuW2bVrFy+//DImkwk/Pz8++eSTun8KnbKYLJxcWnHbXOS05pVX7+pdSkqKzEdtBZKz+lJSUvD3D8BgMODkZCA83IclS0bi7+/J8OHS3BuC7Mf6VmOTf//993F1dWXGjBmsXbuWvLw8br/99hpXbDabmTdvHitWrCA4OJg777yTwYMHV7nVIi8vj7lz5xITE0NYWJjdHSFI35BJcUoJxhaehNwi/wiEsLaUlAL++c+V3HtvZ6ZN6wnAffdFa1yVENZTY5N3c6uYtMXZ2bmyucfGxjJw4MCrfl98fDwRERGEh4cDMGLECLZt21alyX/77bcMHTqUsLCKOdwDAgLq9yl0SFEUTv45hW2rqeEYnG1jFC+EPbBYFN59dxdPP72d0lILGRmFPPjgdbi6OmtdmhBWddUmv3XrVlJTUxk4cCAtWrRg586dvPnmm+Tn59fY5DMyMggJCal8HRwcTHx8fJVlkpKSMJlM3H///RQWFvLAAw8wZsyYGou+9KlH7dq1A+DIkSOV74WGhhIWFkZ8fDzl5RVPjTIajbRv357k5GSysrIql+3cuTNFRUUkJiZWvte8eXOCgoKqbMfHx4eoqChOnDhR5b7Q7t27k5mZSUpKSuV7kZGRFMeVknewACdfA+ci01CSTURERHD48GGKiooAcHV1JTo6mtTUVNLS0nTzmSIjI6/4mYxGIwkJCZXvBQYG2sxnqu7npOVnioiIICcnx64+kx5+Tr/8cphHH/2effsuAHDPPR1YuPBG4uP/sNnPpPefU1xcnN19Jr3+nOrKoCiKcqUvvPzyy2zbto2OHTty4sQJBg0axFdffcUjjzzC2LFjcXV1veqKN23axI4dO3jppZcAWLNmDfHx8cyePbtymXnz5nHgwAFWrlxJSUkJ99xzD0uXLqVly+rniI6Li7OJqzl33b2P87/k0HZWKyIfsa3HUZaVlVUewRHqkZwblsWisGjRbmbN2kZRUTnBwY14772/ceednbQuza7Jfmwd9e191Y7kt2/fztq1a/Hy8iIzM5PBgwezZs0aIiMja7Xi4OBg0tPTK19nZGQQHBxcZZmQkBB8fX0xGo0YjUZ69OjBkSNHrtrkbUHO/jzO/5KDi5czze+3vcfJJiQk2MQfUrZOcm5YZrOFlSv3U1RUztixnXj33VtISjqsdVl2T/Zjfav2FjpPT0+8vLwACAoKokWLFrVu8FBxmCEpKYnTp09TVlbGhg0bGDx4cJVlhgwZQlxcHCaTieLiYuLj4+u0Db3661x88/vCcPW5+hEPIUT9WSwKBQVlALi6OrNq1Ri+/vr/+OyzOwgIMGpcnRDaq3Ykn52dzRdffFH5uqCgoMrru+++++ordnFh9uzZTJ48GbPZzB133EHr1q1ZvXo1AGPHjiUyMpL+/fszatQonJycuPPOO2nTps21fiZNFZ4sIv27TJzcDLSYHK51OULYraSkHCZOXIufnydffXUXBoOBTp2a0KlTE61LE0I3qm3yPXv2ZM+ePZWve/ToUfnaYDDU2OQBBg4ceNkFemPHVp0bevLkyUyePLlORevZySUpoEDY7SF4hNjm4ykDAwO1LsEhSM71Y7EoLF26hyef3EphYTlBQUZSU/Np2tT7smUlY/VJxvpWbZN/7bXXrFmHXSjJKOXsV+lggFYP28YUtlcSEWFbFwraKsm57pKTc5g0aR3btp0C4K67OrBo0XCCghpdcXnJWH2Ssb7Jk2YaUNKyM1jKFIKHBeIVabvnAw8flouVrEFyrpuYmL106vQ+27adIiDAky++uJMvv7yr2gYPkrE1SMb6VuNkOKJ2yvNMpHx8FqiYwtaW/XXvp1CX5Fw3hw5lUlBQxu23t2fx4uEEB3vV+D2SsfokY32TJt9AUj45iynfjH8fX3yv89G6HCFsnqIoVc61v/jiYPr3b86YMe1s5jkQQmit1ofrL50ZSFRlLrVUPk7W1kfxQI0THYmGITlX78yZPG655VP69FlGXl4pAEajK7fd1r5ODV4yVp9krG81NvmEhAQGDx7MqFGjKl/PmTNH9cJsSerX6ZRmlNG4gxeBg/y1LueaRUfLAzysQXK+nKIorFixj44dF7N5cyIFBWUcPHiu3uuTjNUnGetbjU3+5Zdf5v3338fPr+JZ6J07d65ya52jU8xKxW1zVFxRbw+HEVNTU7UuwSFIzlWdPZvHyJGrmThxHXl5pYwa1ZaDB6fRp0/955uQjNUnGetbjU2+rKyMtm3bVnlPDs9clLE5i8KTxXiGexB6q308TvbSBywI9UjOF3311SE6dlzMd98dx9fXg48/vo01a+4mNLTxNa1XMlafZKxvNV545+rqSnFxceUINTExUZr8nxRFIXFxMgAtHwrHyUXuSBSiPtzdncnNLWXkyDYsXTqSsLBra+5CiAo1NvmpU6cyYcIEzp07x3PPPUdsbCyvvPKKNWrTvQs7c8j9Ix83f1fC7wnVuhwhbIaiKOzfn0HXrhWPo7711rZs3z6efv3s45SXEHpRY5MfNGgQLVq0YMeOHSiKwqRJk2jVqpU1atO9xD8fRBMxsRnOns4aV9Nw/noGslCXo+acnl7AlCnrWb/+GL/+OolevZoC0L9/w8+c5qgZW5NkrG81Nvn169dz8803c//991ujHpuRdzCfrNgLOBudiRjXVOtyhNA9RVFYvfoAjz66kQsXivH2dufs2TxA/v0IoZYaTyJv3LiRQYMGMXv2bPbv32+NmmzCX6P48HtDcfOzr2sUjhw5onUJDsGRcs7IKOCOO77k73//hgsXihk2LJIDBx7mttvaq7pdR8pYK5KxvtU4kl+0aBEXLlxg3bp1zJkzh7KyMm6//Xa7enJcXRWlFJP27TkMLgZaPiSPkxXiamJjk7jjji85f76Yxo3dePPNYUya1E3OvQthBbW6HNzf35/x48fz0Ucf0aNHD9544w2169K1U0tPgwXCxgTjGeahdTlC6Frr1gGYzQo33dSKAwemMXnyddLghbCSGkfyiqKwY8cOvvnmG3bt2sWgQYNYtWqVNWrTpdLzZZz+ouK+UFt+nOzVhIbKnQLWYM85b958gptuaoWzsxNhYY3ZvXsyUVH+Vm/u9pyxXkjG+lZjkx8wYAAtW7bktttu4+WXX8ZotN1HqDaE5OVnsJRYaHJTAI3bVv+IS1sWFhamdQkOwR5zzsws5B//+I7//OcQCxfexJNP9gUqRvNasMeM9UYy1rcaD9evXr2ajz76iNtuu83hG7yp0ETyyorHybaygwfRVCc+Pl7rEhyCveX89dcVs9b95z+HaNTIFT8/T61LsruM9Ugy1rdqR/J//PEHXbt2JSkpiaSkpMu+3q9fPzXr0qXTn6VRnmvCr6cP/r18tS5HNeXl5VqX4BDsJefz54t45JGNfP75AQBuvLEFy5aNomVLP20Lw34y1jPJWN+qbfJffPEFXbt2ZfHixZd9zWAwOFyTt5RZOPXBacC+R/FC1MXx4+fp338FGRmFGI2uLFx4Ew8/3BMnJ7mwTgg9qLbJ/zV17WeffWa1YvQsdW0GJWmleLUx0mSINucXrcXRT8tYiz3k3KqVH1FR/rRrF8jy5aNp1Ur70ful7CFjvZOM9a3Gc/L33Xdfrd6zZ4pF4eT7lzxO1s5HKe3bqztBiahgqzmvW3eUM2fyAHB2dmLdurH88MM43TV4sN2MbYlkrG81NvnCwsIqry0WCxcuXFCtID06/2sOBceK8Ah1J2x0sNblqC45OVnrEhyCreV84UIx99//X0aP/pwHH/wWRVEA8Pf31O3heVvL2BZJxvpW7eH65cuXs3z5cnJycqqcfy8uLmbYsGFWKU4v0tZmANDs/0JwcrP/x8lmZWUREdHwDwsRVdlSzuvXH+Ohh74lLa0AT08Xhg2LRFFA73Pa2FLGtkoy1rdqm/wdd9zBkCFDmDdvHnPmzKl838vLC39/f6sUpweWMgvpGzIBCB1l/6N4IS6Vk1PCY49tYtWqiudW9O0bzooVozW7710IUTfVNnkfHx98fHxYtmyZNevRnawdFyjPNdG4XSO7nfxGiCspLi6na9clJCfn4uHhwksvDeaf/7weZ2f7P5olhL2otsnPmjWLBQsWcPfdd19xKsrPP/9c1cL0InXtOQBCRzfRuBLr6dy5s9YlOAS95+zp6cp990Xzww+nWLFiNG3bBmpdUp3pPWN7IBnrW7VN/t577wXgscces1oxemMuNpOxOQuAMAc6VF9UVISbm5vWZdg9Pea8efMJFAX+9rcoAObMGcjcuYNsdvSux4ztjWSsb9U2+ejoaAD69OlT+Z7JZCIvL89hzsmf23Yec6EZn66NMUZoP0WntSQmJtK9e3ety7B7eso5L6+UmTM3ExOzj+DgRhw8OI2AACOurs5al3ZN9JSxvZKM9a3GP8+feOIJ8vPzKSkpYeTIkQwdOpSVK1daoTTtpa2rOFTvCLfNCce1dWsinTotJiZmH25uzjz+eG98fOQRykLYgxqb/PHjx2ncuDGxsbH07NmTHTt28PXXX1ujNk2V55s4t+08GCB0pOOcjxeOIz+/lClTvuXmmz/h9Ok8evYMY9++KTz9dD9cXGzz8LwQoqoaHzVrNpsB+P333xk4cCBGoxEnJ/v/BXBucxaWUgv+vX3xCHXXuhyrat5c5ua3Bq1zHjXqc376KQlXVyfmzh3Ek0/2tbvmrnXGjkAy1rcam3zLli2ZOnUqx44dY8aMGZSUlFijLs2l/jkBjiNdVf+XoKAgrUtwCFrn/K9/DaCgoIwVK0bTqZN97udaZ+wIJGN9q/HP9oULFzJmzBhWrlyJ0WgkJyeHxx9/3Bq1aabsQhlZO7IxOBsIHeF4O3BcXJzWJTgEa+f844+neOWVHZWvBw9uya5dk+22wYPsy9YgGetbjSN5T09P+vTpQ3x8PMnJyURHRzNo0CArlKad9O+yUEwKQYP8cfOXW0OEbSsoKGPWrO9ZtOh3AAYNakGfPuEAup1zXgjRMGps8jt37mTmzJm0bt0aRVFITEzkjTfeqHJrnb1x5EP1wr7ExiYxYcJaTp3KwcXFiX/9awA9eoRpXZYQwkpqbPJvvPEGq1atok2bNkDF1fZPP/0033zzjerFaaEkvZQLv+Xg5O5E8DDHO1QPFVMaC/WpmXNhYRnPPLONd9/dDUDXriGsXDmaLl1CVNumHsm+rD7JWN9qbPLl5eWVDR6gdevWlVfc26O09edAgaAb/XH1rjEeuxQVFaV1CQ5BzZyfe+4H3n13Ny4uTjz3XH+efbY/bm62PbFNfci+rD7JWN9qvPDOz8+PtWvXVr5et24dvr6+qhalpbQ/56oPG+O4E+CcOHFC6xIcgpo5P//8AG66qRW7d0/mhRcGOWSDB9mXrUEy1rcam/wLL7zAqlWr6NatG926dWPVqlXMmzfPGrVZXVFKMTn78nA2OtNkiOM+SjM3N1frEhxCQ+a8c+dp7rrrP5SVVRxlCww0snXr/XTrFtpg27BFsi+rTzLWt1rdJ//NN9+Ql5cHgLe3t+pFaeWvaWyDbw7E2dMxRz7CthQXl/P88z/w1lu/oSjQv39zpk+/XuuyhBA6UW2TT09P59VXX+XUqVN06NCBJ598Ej8/P2vWZnWVV9WPkavqhf79+utpxo9fy7Fj53F2NjBrVj+mTJEHhQghLqr2cP3zzz9PQEAA06dPR1EUFi5caM26rC7/WCH5hwtx8XEhaIBjPGWvOvJEKeuob84lJSaeemor/fqt4Nix83ToEMSvv07ixRcH4+7umBeLVkf2ZfVJxvpW7W+EjIwMYmJiABg4cCB33nmn1YrSQtqfo/iQW4JwcrOv+bvrKjMzU6aqtIL65vz114d47bWdODkZmDWrL3PmDMLDQ5r7lci+rD7JWN+q/c3g4nLxS87O9n1+WlEUUv96rKwcqiclJUX+0VpBXXJWFAWDoWJ2unvv7czOnad54IEuXH99MzVLtHmyL6tPMta3apt8UlIS99xzT7WvP//8c3Urs6K8AwUUnSrGLciNgD72fd2BsD2//36Wf/zjO1avvoPISH8MBgOLFo3QuiwhhA2otskvXrzYmnVoKnXNnxfcjQzC4CxzeQt9KC01MXduLAsX/oLZrPDiiztYsWK01mUJIWxItU3enuemv5RiUUj79s9D9aMddwKcS0VGRmpdgkO4Ws579qQyfvwaDh7MxGCAmTP7MH/+jVaszj7Ivqw+yVjfHP5qney4XEpSS/Fo6o7vdfY7B0BdGI1GrUtwCFfKuazMzPz5sbzyys+YzQqtW/uzYsVo+vZtrkGFtk/2ZfVJxvrm2JeRA2lr/hzFjwrGII/dBCAhIUHrEhzClXJOTLzAwoU7sVgUHn+8N3/8MVUa/DWQfVl9krG+OfRI3mKykLahosnLY2WFVsrLzbi4OGEwGGjfPohFi4bTrl0g/fpJcxdCXJtajeR3797N6tWrATh//jwpKSm1Wvn27dsZNmwYQ4cO5YMPPqh2ufj4eDp06MCmTZtqtd6GcuHXHMqyymnUyhPvjl5W3bYQAH/8kU7Pnh/y+ecHKt+bPPk6afBCiAZRY5NftmwZb775JitWrACgtLSUWbNm1bhis9nMvHnziImJYcOGDaxfv/6KTysym828/vrr9O3btx7lX5vUNX+N4oMr70EWEBgYqHUJdq+83Mwnn5yhZ88P2b8/48+55xWty7I7si+rTzLWtxqb/Nq1a/n4448rL64ICwsjPz+/xhXHx8cTERFBeHg4bm5ujBgxgm3btl223Mcff8ywYcMICLDuU9/MpRbSN2UCEDZKDtVfKiIiQusS7Fp8fAbXXx/D22//gclk4dFHe/Hjj+PkD00VyL6sPslY32o8J+/h4YGrq2uV92rzyygjI4OQkJDK18HBwcTHx1+2zPfff89HH31Up4s34uLiKv+/Xbt2ABw5cqTyvdDQUMLCwoiPj6e8vByouAK0ffv2JCcnk5WVRenucky5JrzaN8IUVF5lnc2bNycoKKjKez4+PkRFRXHixIkqj1bs3r07mZmZVU5hREZGYjQaq3ymwMBAIiIiOHz4MEVFRQC4uroSHR1NamoqaWlp1/yZ/tK5c2eKiopITEys12cyGo0EBgba1WfSw8/pxIlTvPHGLmJijmEyKTRtamTJklsIDS3lyJEEm/xM9vhzks8kn0nPn6nOlBo8+uijyr59+5QxY8YoFotFWbp0qfLYY4/V9G3Kxo0blWeffbby9X//+19l7ty5V1y3oijK008/rWzcuLHG9e7Zs6fGZWpj77QDyoamPygn3ktqkPXZk4bKWFRVVFSmtGnzrgIvKNOmrVe2b/9N65LsnuzL6pOMraO+Odc4kn/uued48sknOX78OF26dKFLly689dZbNf7xEBwcTHp6euXrjIwMgoOrTjZz4MABZsyYAUB2djaxsbG4uLhw00031fVvlToxFZk5t6Xir6VQOVQvVGQyWSgtNdGokRuenq58/PFtFBSUMXhwyyqjCyGEUEONTT44OJiPPvqIgoICFEWhcePGtVpx586dSUpK4vTp0wQHB7NhwwbeeOONKsv88MMPlf8/a9YsBg0apHqDBzj3fRbmYgu+3b0xhnuqvj1b87+nZ0T9HDqUybhxa4iObsKyZRXT0fbq1bTy65Kz+iRj9UnG+lZjk//555+v+H6/fv2uvmIXF2bPns3kyZMxm83ccccdtG7duvJWvLFjx9aj3IaRtvbiBDjictHR0VqXYNNMJgtvvLGT2bN/oqzMzLlzhWRnF+PnV/UPSslZfZKx+iRjfTMoytXv27n33nsr/7+0tJRjx47Rvn17vvzyS9WLu5K4uDi6d+9e7+8vzzOxrevPWEwKg3+/AY9g9waszj6kpqYSFhamdRk26fDhTMaPX8vu3WcBePDB63j99Zvx9r58P5Oc1ScZq08yto769r4aR/KfffZZlddHjx5l1apVdd6QXmRsysRSphDQ11cafDXS0tLkH20dKYrC66/v5F//+pHSUjPNmnkTE3Mrw4ZFVfs9krP6JGP1Scb6Vudpbdu2bcvBgwfVqMUqUv88VB8qh+pFAzIYDBw+nEVpqZmJE7vy5pvD8PHx0LosIYSDq9M5eYvFQkJCAs7OzqoWpZbS82Wc/zkbg4uBkOFBWpcjbJzZbCE9vYCmTSueXvjWW8O4++6OVx29CyGENdXY5BcvXlz5/87OzkRERPD222+rWpRa0jdkopgVgoYE4OYnV4RW568JHET1jh07z4QJa7lwoZi9ex/C09MVHx+POjV4yVl9krH6JGN9u2qTt1gsTJ06lQEDBlirHlWlrc0AZBpbUX9ms4V33tnFs8/+QEmJidBQLxITs+nUSfYpIYT+XHXueicnJ958801r1aKq4tQSLuzOxcndieBh8kCFq7l0CkZx0YkTFxg0aBUzZmyhpMTEAw904eDBafVu8JKz+iRj9UnG+lbjA2ratm3LgQMHalpM99LWnwMFmtwUgItXna83FA4uJmYv0dHv8/PPKYSEeLFu3T2sWjXmsnvfhRBCT2rsdseOHePuu++mVatWNGrUqPL9zz//XNXCGlpa5VX1clhV1J2npwvFxSbuuy+af//7b/j7S3MXQuhfjU3+qaeeskYdqio8VUTu/nxcvJxpMti6j7S1RaGhoVqXoDmLReGPP9K57rqKLO69tzOtWvnRp094g21DclafZKw+yVjfqm3yzz77LC+//DJ9+vSxZj2qSEKN0rAAACAASURBVPu2YhQfPCwQZ0/bvP3Pmhx9YouTJ7OZNGkdO3eeZt++KXToEITBYGjQBg+SszVIxuqTjPWt2nPyhw8ftmYdqpIJcOomPj5e6xI0YbEoLF78O9HR7/PTT0n4+nqQlpav2vYcNWdrkozVJxnrm91fgZZ/pICCo4W4+roQOMBP63JsQnl5udYlWF1SUg6TJq3jhx9OAXD33R15773hBAYaVdumI+ZsbZKx+iRjfau2yR87duyKh+oVRcFgMPDrr7+qWlhDSV1XMYoPGdEEJ9cabyYQDmjduqP8/e/fUFBQRmCgkfffH8Gdd3bQuiwhhLhm1Tb5Fi1a8MEHH1izlganKMolj5WVq+pry2hUb/SqRx06BGE2W7jzzg4sWjScJk0a1fxNDcDRctaCZKw+yVjfqm3ybm5uNG3a1Jq1NLjc/fkUJRfjHuyGf29frcuxGe3bt9e6BFUpisL69ccYObINBoOBqCh/EhIeJjLS36p12HvOeiAZq08y1rdqj1+7utr+3O5pfx6qDx3ZBIOzQeNqbEdycrLWJajm9OlcbrnlU0aN+pyYmL2V71u7wYN956wXkrH6JGN9q7bJf/nll9aso8EpFuVik5dD9XWSlZWldQkNTlEUli3bS6dO77N5cyL+/p6az1ZnjznrjWSsPslY3+z26vrCk0WUpJfiEeKO73XeWpcjNHTmTB4PPvgtmzadAGD06LYsWTKSkBAvjSsTQgh12W2TLz5dAkCjSE8MBjlU76j27UvjxhtXkZtbip+fB+++ewv33ttZ9gkhhEOw3yZ/tqLJezbz0LgS29O5c2etS2gwHTs2oUULX5o392Hp0pGEhjbWuqRK9pSzXknG6pOM9c1+m/yZP5t8U2nydVVUVISbm5vWZdSLoih89lkCw4ZFERhoxM3NmR9+GIefn4fuRu+2nLOtkIzVJxnrm93ODlN8phSQkXx9JCYmal1CvaSl5TN69Ofcd99/efTRjZXv+/vr85SNreZsSyRj9UnG+ma/I/mzMpJ3FIqi8OmnCUyfvpHs7BJ8fNwZNiyycnZGIYRwVPbb5M/IOXlHkJ5ewNSp61m79igAt9wSxYcf3krTpnJHhRBC2GWTt5RbKMkoBQN4hLprXY7Nad68udYl1MqFC8V06rSY8+eL8fZ25623hjFhQlebGb3bSs62TDJWn2Ssb3bZ5EvSSsECHiHuOLnZ7WUHqgkKCtK6hFrx9/fknns6cfz4BWJibiU83EfrkurEVnK2ZZKx+iRjfbPLDnjx9jkZxddHXFyc1iVU68svDxIbm1T5+s03h7Fp099trsGDvnO2F5Kx+iRjfbPLkXzx2Yor6z3koju7kZlZyLRp3/HVV4do0cKXAwceplEjN9zcnLUuTQghdMs+m7xcdGdXvvrqENOmbSAzswgvLzdmzeqL0Wj7D1ASQgi1SZMXl/Hx0ceh76ysIv7xj+/48suDAAwe3JJly0bRooV9PDZYLznbM8lYfZKxvtlnk5d75K9JVFSU1iWgKApDhnxEfHwGjRq5snDhUKZO7YGTk21cOV8besjZ3knG6pOM9c0+L7yTkfw1OXHihNYlYDAYeOGFgQwa1IKEhIeZNq2nXTV40EfO9k4yVp9krG92N5JXLAolqTKl7bXIzc3VZLtr1hzh1KlsHn+8DwC33daeMWPa2cx973WlVc6ORDJWn2Ssb3bX5MuyyrCUWnD1c8XFKFde24ILF4qZPn0jn36agLOzgWHDoujQoeLeW3tt8EIIYQ121+QvHqqXe+Rtwbp1R5kyZT3p6QV4erqwYMFNtGsXqHVZQghhF+yvyf95j7xcdFd/3bt3V30b2dnF/POfm/j443gA+vYNZ8WK0bRuHaD6tvXCGjk7OslYfZKxvtndhXdy0d21y8zMVH0bfzV4Dw8X3nzzZmJjxztUgwfr5OzoJGP1Scb6Jk1eXCYlJUX1bbz00mD+9rco9u+fyuOP98HZ2e52xRpZI2dHJxmrTzLWN7v7zSr3yOvTxo3Hueeer7BYFADCw33YuPHvtGnjWKN3IYSwJvtr8jKS15Xc3BImTVrL8OGf8cUXB1m9OkHrkoQQwmHY4YV30uSvVWRkZIOsZ/PmE0ye/C1nzuTh7u7M/Pk3cs89nRpk3fagoXIW1ZOM1ScZ65tdNfny3HJM+Wacjc64+trVR7Mqo9F4Td+fl1fKzJmbiYnZB0CvXv/f3p2H13infxx/n+wEQZmQUmOpUluIJFqDCqqRHVWqiyVTS6kqU0tLaU3VVr/aaxlLi5kikkiYGjtTlEjFltYWW4gtkYisJ9/fHxmnUktOJE/Okvt1Xa5LTp7lPp+Q+zzfZ/k+y4oVQTRuLPNOP6i4OYvCScbak4zNm1UN1xtun6vlKA9RKYZjx4o3pL5y5S8sXRqLg4MtU6d24r//HSAN/hGKm7MonGSsPcnYvFnV4a7hfLybDNWXNqWU4YPVkCGeHDt2nREjvGnS5E8mrkwIIcou6zqS/1+Td5Lz8aVq+/ZzeHgsJjExDQA7OxsWLw6QBi+EECZmXU1eLrorEdWqGfdY2bt3sxk6NJrOnb8jNvYas2b9pHFl1sXYnMXTk4y1JxmbN+scrpd75IulTp06hS6zc+d5BgyIJCEhBXt7GyZMaM/YsX8pheqshzE5i+KRjLUnGZs36zqSl8lpSsSpU6ce+7309GyGD9+Mj88qEhJScHevweHD7zFhQgfs7WXWv6J4Us6iZEjG2pOMzZt1HcnL0+5KxL179x77vZMnb7BgwWHs7Gz49NN2jB/fTpr7U3pSzqJkSMbak4zNm9U0eX2GnuybOejsdDi5ypF8ScrO1uPgkN/IPT2fZe5cX15+uTbu7jVMXJkQQogn0XS4fs+ePXTt2pUuXbqwePHih74fGRlJQEAAAQEB9O7dm/j4+KfeV0Zi/j3yTm6O6GzlHvnisLe3N/x9794LNGmygOjo3wyvDR3qKQ2+BDyYs9CGZKw9ydi8adbk9Xo9n3/+OUuXLiU6OpqoqCjOnDlTYJlatWrx/fffs2nTJoYMGcKECROeen9y0V3Jad68Offu5TBy5L/p0GEFZ87cZu7cn01dltVp3ry5qUuwepKx9iRj86ZZk4+Li6NOnTrUrl0bBwcH/Pz82L59e4FlWrVqhYuLCwDu7u5cu3btqfcnE9OUnIiIWNzdF/F//3cQGxsdn3zSjoiI3qYuy+okJiaaugSrJxlrTzI2b5o1+aSkJGrU+H1I19XVlaSkpMcuv379etq3b//U+5OL7oovIyOH0aO3EhISyenTt2nSpDoHDoQyZYoPjo5Wc/mG2bh69aqpS7B6krH2JGPzZha/uQ8cOMD69etZs2aNUcvHxMQY/t6oUSMArsTlf5q8rr9GhURH3NzciIuLIycnB8ifRKFx48ZcuHCBmzdvGtZv1qwZ9+7d4+zZs4bXnnvuOapXr15gPy4uLjRo0IAzZ85w584dw+seHh7cuHGDixcvGl6rX78+5cuXL/BM52rVqlGnTh1OnTpluBrV3t6e5s2bk5iYWOA/yv339OA1CjVr1tT8Pd29m8P338ei08EHH7TijTdc0emuEhNz1WLfkzn/nABSUlKs6j2Z488pPT3d6t6Tuf2cYmJirO49mevPqah0Sin11Gs/QWxsLPPmzWPZsmUAfPvttwAMGjSowHLx8fEMGzaMJUuWULdu3UK3GxMTg4eHx0Ov7+9xhOSDd/D6Zwuq/aVqCbyDsiEzMxcAJ6f8z3t79lzg/PnTvPtuZ1OWVSY87t+yKDmSsfYk49LxtDlrNlzfrFkzEhISuHTpEtnZ2URHR+Pj41NgmcTERIYPH8706dONavBPIhfeFd3Bg5dp2fJbPv10h+G19u3r0LPnSyasquy4/ylfaEcy1p5kbN40G663s7Nj4sSJhIaGotfr6dGjB88//zxr164FoE+fPsyfP5+UlBQmT54MgK2tLWFhYUXeV15uHlnXsgFwkhnoCpWVlcukSbuYPv0n8vIUNjY6pkzxMRzNCyGEsA6aDddr5VFDFhmXM9nZZj+Org50imlrososw6FDV+jXL4KTJ2+g08Ho0S8zefIrlCv3+72uMvxWOiRn7UnG2pOMS8fT5mwVh24yVF84vT6PiRN3Mm3af9HrFQ0bPsOKFUG89FJtU5cmhBBCI9bV5OUe+ceysdFx8uRN8vIUo0a9xBdfdCxw9C6EEML6WEeTl3vkHyk7W8+NG+k8+2wldDodixb5MXr0S7Rt+9wT16tZs2YpVVi2Sc7ak4y1JxmbN+to8nIk/5DY2Kv06xeBvb0N+/cPxN7eFlfXCri6Vih0XTc3t1KoUEjO2pOMtScZmzermE/ecCQv88iTna1n0qRdeHktJS4uieTkTC5fTi3SNuLi4jSqTjxIctaeZKw9ydi8WdeRfBkfrj969Br9+kXwyy/5cwAMH+7F1KmdcHZ2KNJ27j+FSWhLctaeZKw9ydi8WXyTV0qRcSV/mtmyPFw/c+ZPjBu3ndzcPOrWrcw//hHEK6/82dRlCSGEMCGLb/LZt3LIy8rD3sUOuwoW/3aeWrlyduTm5vH++5589VVnKlQo2tH7g8qXL1+ClYnHkZy1JxlrTzI2bxbfFcvqRXe5uXnExSXRqlX+la1Dhnji5fUsnp7PFnvbjRs3LvY2ROEkZ+1JxtqTjM2bxV94Vxab/PHj12nTZikdOqwgISEFyL8PviQaPMCFCxdKZDviySRn7UnG2pOMzZvlN/kydI98bm4eU6fuxcNjMTExV6latRzXr6eX+H7+OB2q0IbkrD3JWHuSsXmzmuF6Jyu/fe7kyRv06xfOoUOJALz3XitmzHiVSpWs+30LIYR4epbf5O9fWW/FR/LffXeU0NBNZGfrqV27EkuXBvLqq/VNXZYQQggzZ/lNvgyck2/e3BWlFKGhLZk581VcXLR9r82aNdN0+yKf5Kw9yVh7krF5s55z8lbU5PX6PCIi4g1ft2hRg99+G86SJYGaN3iAe/fuab4PITmXBslYe5KxebPoJp+TlkvunVxsnGxwqGodM6r99tst2rVbTnDwv1i37oTh9T//uXKp1XD27NlS21dZJjlrTzLWnmRs3ix6uP7BoXqdTmfiaopHr89jzpyDjB+/g8zMXNzcKpbKUbsQQgjrZdlN3kpunzt9+hb9+0fw3/9eAqBfP3dmz+5K5cqW/b6EEEKYlmU3eSu46G737gR8fVeTkZFLzZoVWLIkAD+/hiat6bnnnjzfvCgZkrP2JGPtScbmzaKbfOZly59itnVrN9zcKvLyy7X55pvXqFKlnKlLonr16qYuoUyQnLUnGWtPMjZvFn3hnSXeI5+Xp1i69Ahpafm1Ozs7cPjwe6xaFWIWDR4gJibG1CWUCZKz9iRj7UnG5s2ym7yFDdefO5eMj89K/vrXTXz88X8Mr8u5dyGEEFqw6OF6S7lHPi9PsXDhIcaM2UZ6eg5/+pOzPLFOCCGE5iy2yesz9WRdz0Znp8PJ1XzPySckpDBgQAQ7dyYA0Lt3U+bO9aVaNfOdg9nFxcXUJZQJkrP2JGPtScbmzWKbfGZi/jltp5qO6GzN8x75S5fu0KzZQu7ezaZ69fIsXOhHjx4vmrqsQjVo0MDUJZQJkrP2JGPtScbmzWLPyVvCPfK1a7sQHNyIXr2acOLEUIto8ABnzpwxdQllguSsPclYe5KxebPYI3lzvOhOKcWSJUfw8KiJh4cbAMuWBeLgYGviyormzp07pi6hTJCctScZa08yNm+W3+SfNY/z8Rcv3iE0NJL//OccTZpUJzZ2EPb2thbX4IUQQlgPy23y9++RN/GRvFKKZcti+eijH0lLy+aZZ8oxYUJ77Ows9kyIEEIIK2G5Tf5/R/JOJjwnf/lyKqGhkfz4Y/4sTCEhjVi40A9X1womq6kkeHh4mLqEMkFy1p5krD3J2LxZ7OGmqe+Rz8nR07btP/jxx7NUrVqO1au7s2FDL4tv8AA3btwwdQllguSsPclYe5KxebPIJq/0isyr/xuudzPNOXl7e1smTepAUNALnDgxlDffbGbx093ed/HiRVOXUCZIztqTjLUnGZs3ixyuz0zKQuUqHKo7YOtUOhe2KaVYteooGRm5DB7cGsifErZfP3erae5CCCGsi0U2+dK+sj4xMY1Bg6KIivoNJyc7unV7nueec5HmLoQQwqxZdpPX+Hy8Uorvv4/jgw/+TUpKJi4ujsyZ40vt2pU03a+p1a8vz9UvDZKz9iRj7UnG5s0ym3wpPO3u2rW7DBoURWTkrwB06/Y8ixf78+yz1t3gAcqXN9/n6lsTyVl7krH2JGPzZpEX3mVc1v4e+dDQSCIjf6VSJUf+8Y9AoqL6lIkGD3Ds2DFTl1AmSM7ak4y1JxmbNwtt8toP18+a9SrBwY04cWIo/fu3lPPvQgijNG7cmKCgIPz9/Rk8eDCpqaklst3Lly/j7+9fItt60Ny5c2nXrh1BQUEEBQUxc+bMEt/HfadOnWL37t0FXtu2bRvz5s0r8FpQUBAjR44s8Nrbb79d4APFH/OIi4ujb9++dO3aleDgYD755BMyMjKKVe+lS5d4/fXX6dKlCx9++CHZ2dmPXG7GjBn4+/vj7+/P5s2bC11/586dfPPNN8WqzViW2eRLeLheKcU//3mcd98NRykFwAsvVGPjxjeoVatsHL0LIUqGk5MTERERREVF4eLiwurVq01dUqH69etHREQEERERjB492uj19Hp9kfbzqCa/dOlS3nzzTcPXZ8+eJS8vj8OHD3Pv3j2jtnvz5k1GjBjB6NGj+fHHHwkPD6ddu3akp6cXqb4/mjlzJv369eM///kPlSpVYv369Q8ts2vXLk6ePEl4eDg//PADy5Yt4+7du09c/5VXXmHnzp3F/hBiDMs8J1+CR/LXr6czdGg0GzacAuCNN5rQrdvzxd6uJatWrZqpSygTJGdtHXrnKDd2pLKZnSW2zeo+VfFc1cLo5d3d3fn11/zretLT0xk6dCipqank5uYyYsQIOnfuzOXLl/nrX/+Kh4cHsbGxuLq6smDBApycnDh+/Djjx48HoG3btobtZmVlMWnSJI4fP46trS1jx46lTZs2hIWFsW3bNjIyMrhw4QIDBgwgJyeHiIgIHBwcWLx4MZUrVzaq9v379zNt2jT0ej1NmzZl8uTJODg44OPjg6+vLz/99BOhoaFUrVqVgQMHkpycjJOTE1988QX169dny5YtzJ8/HxsbGypWrMjy5cuZM2cOmZmZxMTEMGjQIBo3boy9vT1Vq1Y17DcqKorAwEDOnTvH9u3bCQgIKLTW1atXExwcTMuWLQ2vvfbaa0a9z8dRSnHgwAFmzZoFQEhICPPmzSvwgQTyZ+Fr3bo1dnZ22NnZ8cILL7Bnzx58fX0fu75Op8PLy4udO3fSrVu3YtVZGIs8ks/LzMOukh32lYr3GWXduhM0abKADRtOUbGiA4sX++PrK3Mj16lTx9QllAmSs3XT6/Xs378fHx8fABwdHZk/fz4bN25k5cqVTJs2zTByeOHCBfr27Ut0dDQVK1bkxx9/BGDcuHFMmDCByMjIAtu+PzqwadMmZs2axdixY8nKyr9W6fTp08ydO5f169cze/ZsnJycCA8Px93dnfDw8EfWumLFCsNw/d69e8nKymLs2LHMnj2bTZs2odfrWbNmjWH5ypUrs3HjRvz8/Fi0aBETJkwgLCyMMWPGMHnyZAAWLFjAsmXLiIyMZOHChTg4OPDBBx/QrVs3IiIi6NatG0eOHKFJkyYFatm8eTN+fn74+fkRHR1tVNanT59+aDuPcu7cOcP7/OOfP55WSU5OplKlStjZ5feZGjVqkJSU9NA2GzVqxN69e8nIyOD27dscPHiQa9euFbp+06ZNiYmJMer9FYdFHslD8e6Rv3Ejnfff38y6dScB6NSpLsuWBVKnjnGfcK3dqVOnaNy4sanLsHqSs7Y8V7UwScaZmZkEBQWRlJRE/fr1DUfgSim+/vprDh06hI2NDUlJSdy8eROAWrVqGeps0qQJV65cITU1lbS0NDw9PQEMDRggJiaGt956C8i/hc3NzY3z588D4O3tTYUK+Y/XrlixouFDRsOGDQ2jCn/Ur18/Bg4caPg6Pj6eWrVqUbduXSD/KHT16tX069cPwHD0mZ6ezpEjRxgxYoRh3fvnnVu2bMnYsWPx9fWlS5cuj9zvjRs3ChzFHzt2jCpVquDm5oarqyvjx48nJSXlsaMPRb1Wql69ekRERBRpncL85S9/4dixY/Tu3ZuqVavi7u6OjU3hx8/PPPMM169fL9FaHsVym3wxhuoXLjzMunUncXa2Z+bMVxk0yEMurHuAsefBRPFIztozRcb3z8lnZGQwcOBAVq9ezTvvvMOmTZu4ffs2YWFh2Nvb4+PjYzj6dnBwMKxva2treP1pPLgtGxsb7O3tDX8v6jn0xylXrhyQ/8GlfPnyj2ycn3/+OUePHmXXrl306NGDDRs2PLSMk5MTaWlphq+jo6M5f/684YPJ3bt32bp1K7169aJKlSoFjrbv3LljaP4NGjTgxIkTdO7c+Yl1nzt37qEL+u777rvvqFTp92uw7u8vNzcXOzs7rl27hqur6yPXHTJkCEOGDAFg1KhR1K1bt9D1s7KycHTU/oFuFjlcD0W/6O7+sBjAmDFtee+9Vhw7NoTBg1tLgxdClLhy5crx6aefsnz5cnJzc0lLS+OZZ57B3t6eAwcOcOXKlSeuX6lSJSpWrMjhw4eB/KH5+1q3bm34+vz581y9epV69eqVWO1169blypUrXLhwAYCIiAjDiMKDKlSoQPXq1dmyZQuQ/3s2Pj4eyH+mfYsWLRgxYgRVqlTh2rVrODs7F7gYrl69eoZ95OXlsWXLFiIjI9mxYwc7duxgwYIFREVFAeDl5UVkZKThd/nGjRvx9vYG4K233iI8PJyjR48atr1161bDSMmD+7t/geEf/zzY4CF/lMDb29tw6mTjxo2GDx8P0uv1JCcnA/kjIL/++itt27YtdP2EhAQaNmz4hJ9CybDcJl+EI/mNG0/h4bGY5OT8KxkdHe349tsA6tatolV5Fu3+J3+hLclZe6bO+MUXX+SFF14gKiqKgIAAjh8/TkBAABEREUY15alTp/L5558TFBRU4EDlzTffRClFQEAAI0eOZOrUqQWO4IvL0dGRqVOnMmLECAICAtDpdPTp0+eRy44YMYL169cTGBiIn58f27ZtA2D69OkEBATg7+9Py5YtadSoEd7e3pw5c4agoCA2b96Mp6cnp06dQinF4cOHcXV1LXC06+npydmzZ7l+/Tq9evXC2dmZwMBAAgMDuXfvnuEUQ7Vq1fj666+ZNm0aXbt2xdfXl3379uHs7FysHP72t7+xfPlyunTpQkpKCq+//jqQf1rhk08+ASA3N5e+ffvSrVs3Jk6cyIwZMwzn4R+3PsDBgwfp0KFDseozhk49+C/HAsTExJAUlErLRU2o6f+nJy5769Y9hg/fwtq1xwH48ksfxo1rVxplCiGEMMKUKVPw8fHh5ZdfNnUppebmzZuMGjWKlStXGr1OTEwMHh4eRd6X5R7JFzJcHxERT5MmC1i79jjly9szd64vY8b8pZSqs2yJiYmmLqFMkJy1Jxlrr7gZDx48uFTuFzcniYmJjB07tlT2ZblN/jHD9bdvZ/D22xsJDv4XSUnptGv3HHFxgxk2zAsbGzn3boyrV6+auoQyQXLWnmSsveJmXK1aNTp16lRC1ViG5s2bl9pdHxZ5db2Now0O1R59ru3Ikat8/30c5crZ8dVXnaW5CyGEKLMsssmXe9axwBXxWVm5ODrmv5XOnesxe3ZX/Pye5/nnnzFViUIIIYTJWeRw/YND9Zs3n6ZBg7ns3XvB8NqHH7aRBl8MjRo1MnUJZYLkrD3JWHuSsXnTtMnv2bOHrl270qVLFxYvXvzQ95VSTJkyhS5duhAQEMCJEyeM2m65Z51ISclkwIAI/PzWcPlyKt9+q/3jAYUQQghLolmT1+v1fP755yxdupTo6GiioqI4c+ZMgWX27NlDQkICW7du5YsvvmDSpElGbftQ2i2aNl3A8uW/4Ohoy4wZXVi5MliDd1E23X+YhdCW5Kw9yVh7krF50+ycfFxcHHXq1KF27doA+Pn5sX37dho0+H0CmO3btxMcHIxOp8Pd3Z3U1FSuX7/On/705PvfB36b/7AFb+9nWbEimEaNZDYvIYQQ4o80O5JPSkqiRo0ahq9dXV0fmsHnj8s8bpafP3Kwt2XatM7s2zdAGrwQQgjxGBZ5df1P+30BOHo01sSVWK/SmAJRSM6lQTLWnmRsvjRr8q6urly7ds3wdVJS0kMz+PxxmSfN8nPf0zzWTwghhCiLNBuub9asGQkJCVy6dIns7Gyio6MfmsHHx8eH8PBwlFL88ssvVKxYsdDz8UIIIYQwjmZH8nZ2dkycOJHQ0FD0ej09evTg+eefZ+3atQD06dOHDh06sHv3brp06UK5cuX48ssvtSpHCCGEKHMsbhY6IYQQQhjHIp94J4QQQojCSZMXQgghrJTZNnmtHokrfldYxpGRkQQEBBAQEEDv3r3lyVZPobCM74uLi+PFF1/k3//+dylWZz2MyfngwYMEBQXh5+fHW2+9VcoVWr7CMk5LS2Pw4MEEBgbi5+fHhg0bTFClZRs3bhwvvfQS/v7+j/z+U/U9ZYZyc3NVp06d1MWLF1VWVpYKCAhQp0+fLrDMrl271MCBA1VeXp6KjY1VPXv2NFG1lsmYjGNiYlRKSopSKj9vybhojMn4/nJvv/22Cg0NVVu2bDFBpZbNmJzv3LmjfH191ZUrV5RSSt28edMUpVosYzJeuHChmj59ulJKqVu3bilPT0+VlZVlinIt1s8//6yOHz+u/Pz8Hvn9p+l7Znkk/+AjcR0cHAyPxH3Q4x6JK4xjTMatWrXCxcUFAHd39wLPNBCFMyZjrc6nQAAACSVJREFUgO+++46uXbvyzDMyc+LTMCbnTZs20aVLF9zc3AAk6yIyJmOdTkd6ejpKKdLT03FxccHOziKft2Yynp6eht+5j/I0fc8sm7yWj8QV+YzJ+EHr16+nffv2pVGa1TD23/G2bdvo06dPaZdnNYzJOSEhgdTUVN5++226d+9OeHh4aZdp0YzJuG/fvpw9e5Z27doRGBjIJ598go2NWbYYi/U0fU8+ZolCHThwgPXr17NmzRpTl2J1/v73vzN69Gj5ZagxvV7PiRMnWLFiBZmZmfTu3ZsWLVpQt25dU5dmNfbt20fjxo1ZtWoVFy9epH///rRu3ZoKFSqYurQyzSybvFaPxBW/MyZjyJ9G8tNPP2XJkiVUqVKlNEu0eMZkfPz4cT766CMAkpOT2b17N3Z2dnTu3LlUa7VkxuRco0YNKleuTPny5SlfvjytW7cmPj5emryRjMk4LCyM9957D51OR506dahVqxbnzp2jefPmpV2u1XqavmeWhw/ySFztGZNxYmIiw4cPZ/r06fLL8CkYk/GOHTsMf7p27cpnn30mDb6IjMm5U6dOxMTEkJubS0ZGBnFxcdSvX99EFVseYzKuWbMm+/fvB+DmzZucP3+eWrVqmaJcq/U0fc8sj+TlkbjaMybj+fPnk5KSwuTJkwGwtbUlLCzMlGVbFGMyFsVnTM7169c3nCu2sbGhZ8+eNGzY0MSVWw5jMh46dCjjxo0jICAApRSjR4+matWqJq7csnz00Uf8/PPPJCcn0759e4YPH05ubi7w9H1PHmsrhBBCWCmzHK4XQgghRPFJkxdCCCGslDR5IYQQwkpJkxdCCCGslDR5IYQQwkqZ5S10QlgLHx8fHBwccHR0BMDb25vx48c/cZ327duzfPnyErmPe/bs2axbt47q1auTnZ2Nh4cHn332Gfb29kXe1urVq9Hr9bzzzjucOHGCS5cu8dprrwH5T5Tr3r0769atw8HBodh1Q34Ozs7O2Nvbk5OTQ2hoKD169Ch0va1bt1KzZk2aNWtWInUIYcmkyQuhsTlz5pj0nuzu3bszevRosrKy6Nu3Lz/88AN9+/Yt8nYeXOfkyZP89NNPhiZva2tLREREidV837x586hfvz7x8fH07NmTDh06UK1atSeus3XrVjw8PKTJC4EM1wthEuHh4fTs2ZPg4GBCQkI4ePDgI5f75ptveO211wgMDCQkJIS7d+8CEBsba5hspXv37uzevbvQfTo6OuLh4cH58+cB2LVrF8HBwQQEBNC/f38uXboEwNmzZ+nVqxeBgYH4+/uzYsUKIH9UYObMmdy6dYv58+ezb98+goKC+PLLL8nNzeWFF14gKyuLsLAwPvjgA8N+c3JyaNu2LVevXkUpxaJFiwzvfciQIdy6davQ2hs1aoSzs7Nhxq1Tp07Rp08fQkJC8PPz47vvvgNg9+7d7Nmzh0WLFhEUFERkZCSQP8FSz549CQkJ4d133yUhIaHQfQphFUpsIlwhxEM6duyounbtqgIDA1VgYKDas2ePUkqp27dvG5Y5ffq06tChg+Hrdu3aqTNnzqhbt24pLy8vlZmZqZRSKi0tTeXk5Kjk5GQVFBSkbty4oZRS6tq1a6pdu3YqLS3tof1//fXXasaMGUqp/DnV/f391YYNG9T169eVl5eXOnPmjFJKqbVr16o33nhDKaXUpEmT1JIlSwzbSElJeWhbP/zwg/rwww8Ny+Tk5KiGDRuqzMxMdffuXeXl5WVYb+vWrap///5KKaU2bNigPvvsM6XX65VSSq1atUp9/PHHj8zufg5KKXXw4EHl7++vsrOzDVncn6s8LS1Nvfrqq+rcuXNKKaVGjRql1qxZY9jOgQMH1KBBgwzLb9++XfXt2/eR+xTC2shwvRAae9Rw/YULFxg1ahTXr1/H1taWpKQkbt++XeAxoJUqVcLNzY0xY8bQtm1bXnnlFSpUqEBMTAyXL19m4MCBhmV1Oh2XLl2icePGD+0/LCyMvXv3otPp6Ny5M8HBwWzfvp2mTZsazvv37NmTKVOmkJGRgaenJ7Nnz+bu3bu0adMGb2/vIr1fZ2dnOnbsSFRUFH379iUsLIyQkBAg/1n9p06dMnyt1+upXLnyY7c1bNgw8vLyuHTpEnPmzDFcS3Dv3j0+++wzfvvtN3Q6HTdv3uTXX3995BwLO3bs4OTJk7z++usAhvnOhSgLpMkLYQIjR45k4sSJdOzYEb1eT4sWLcjOzi6wjJ2dHevWrSMmJoYDBw4QEhLC8uXLUUrx4osvsmrVKqP2df+cvLG6deuGh4cH+/btY9GiRYSHh/PVV18V6f2FhIQwc+ZMfH19iY2NZfbs2UB+gx0+fDjBwcFGbef+OfmoqCjGjh3L1q1bqVq1KrNmzaJmzZpMnz4dW1tb3nnnHbKysh65DaUUvXr1YtiwYUV6D0JYAzknL4QJpKWlGWbo+uGHH8jJyXlombt375KcnIy3tzcjRoygXr16nD59mlatWnH27FkOHTpkWPbo0aNF2r+7uzsnTpwwnJ8PCwujWbNmlCtXjoSEBKpXr06PHj0YOnQocXFxD63v7OxMWlraY7fv5eXF7du3mT17Nq+++ipOTk5A/t0Gq1evJjU1FYCsrCzi4+MLrdff3x9vb2+WLFkCQGpqKjVr1sTW1pb4+HiOHDliWLZChQoFauvYsSPh4eEkJSUB+aMHx48fL3SfQlgDOZIXwgTGjx/PoEGDcHFxoUOHDlSsWPGhZe7cucOHH35IZmYmSimaNm1K586dcXBwYMGCBcyYMYPU1FRyc3OpXbs23377rdH7r169Ol999RUjR44kLy+PqlWrMn36dACio6PZvHkz9vb26HS6R97y17ZtW1auXElgYCBt2rTh448/LvB9nU5HcHAw8+bN41//+pfh9R49epCSkmK4Ul8pxVtvvUWjRo0KrXnUqFH06tWL0NBQ3n//fcaMGcM///lP6tWrR+vWrQ3LBQcHM378eDZv3syAAQMIDAxk2LBhDBo0iLy8PHJzc+nWrRtNmzY1Oi8hLJXMQieEEEJYKRmuF0IIIayUNHkhhBDCSkmTF0IIIayUNHkhhBDCSkmTF0IIIayUNHkhhBDCSkmTF0IIIayUNHkhhBDCSv0/nGmSlqromtgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1180842b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b796588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotAUC(y_test,rfPredictproba, 'Random Forest')\n",
    "plt.show()\n",
    "plt.figure(figsize=(6,6))\n",
    "plot_confusion_matrix(rfPredict, normalize=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression with Grid Search\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "<span style=\"color:blue\">\n",
    "Logistic Regression when implemented with grid search we got the best accuracies and minimum false negatives. We used the randomized search to find the best hyper paramters for the model.\n",
    "</span>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best accuracy is 0.831818181818\n",
      "LogisticRegression(C=100, class_weight=None, dual=False, fit_intercept=True,\n",
      "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
      "          penalty='l2', random_state=0, solver='liblinear', tol=0.0001,\n",
      "          verbose=0, warm_start=False)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "def cross_validation_best_parameters(model, param_grid):\n",
    "    grid = GridSearchCV(model, param_grid,cv=10, scoring='accuracy')\n",
    "    X=data_clean.iloc[:,:-1].values\n",
    "    y=data_clean.iloc[:,-1].values\n",
    "    grid.fit(X,y)\n",
    "    mean_scores = [result.mean_validation_score for result in grid.grid_scores_]\n",
    "    return mean_scores,grid.best_score_,grid.best_estimator_\n",
    "logreg = linear_model.LogisticRegression(random_state=0)\n",
    "c=[0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "param_grid = dict(C=c)\n",
    "mean_scores,Best_Accuracy, Best_classifier = cross_validation_best_parameters(logreg,param_grid)\n",
    "print(\"Best accuracy is \"+ str(Best_Accuracy))\n",
    "print(Best_classifier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic regression accuracy is  0.827727272727\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118084a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x129e2b208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf_LR = linear_model.LogisticRegression(C=Best_classifier.C)\n",
    "clf_LR.fit(X_train,y_train)\n",
    "LR_Predict = clf_LR.predict_proba(X_test)[:,1]\n",
    "LR_Predict_bin = clf_LR.predict(X_test)\n",
    "LR_Accuracy = accuracy_score(y_test,LR_Predict.round())\n",
    "print(\"Logistic regression accuracy is \",LR_Accuracy)\n",
    "plotAUC(y_test,rfPredictproba, 'Random Forest')\n",
    "plotAUC(y_test,LR_Predict,'Logistic Regression')\n",
    "plt.show()\n",
    "plt.figure(figsize=(6,6))\n",
    "plot_confusion_matrix(LR_Predict_bin, normalize=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Support Vector Machines(SVM) with Grid Search CV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "<span style=\"color:blue\">\n",
    "SVM (Support Vector Machines) when implemented with grid search, we got the best accuracies and minimum false negatives. We used the Grid search to find the best hyper paramters for the model.Later we used this value to find the predictions and plot the ROC curve.\n",
    "</span>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fitting done\n",
      "{'C': 10}\n",
      "---------------\n",
      "SVC(C=10, cache_size=200, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n",
      "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
      "  tol=0.001, verbose=False)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.grid_search import GridSearchCV    ## takes too much time to run this cell.\n",
    "clf_svm = svm.SVC()\n",
    "powers = range(0,5)\n",
    "cs = [10**i for i in powers]\n",
    "param_grid = dict(C=cs)\n",
    "grid = GridSearchCV(clf_svm, param_grid, cv=10, scoring='accuracy')\n",
    "grid.fit(data_clean.iloc[:,:-1].values, data_clean.iloc[:,-1].values)\n",
    "grid_mean_scores = [result.mean_validation_score for result in grid.grid_scores_]# create a list of the mean scores only\n",
    "print(grid.best_params_)\n",
    "print(\"---------------\")\n",
    "print(grid.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM accuracy is  0.825909090909\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12b487048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11811c048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf_svm = svm.SVC(kernel = \"rbf\", C=grid.best_estimator_.C)\n",
    "clf_svm.fit(X_train.iloc[:,:],y_train)\n",
    "predictions_svm = clf_svm.predict(X_test.iloc[:,:])\n",
    "predictproba_svm = clf_svm.decision_function(X_test.iloc[:,:])\n",
    "SVM_Accuracy = accuracy_score(y_test,predictions_svm)\n",
    "print(\"SVM accuracy is \",SVM_Accuracy)\n",
    "plotAUC(y_test,predictproba_svm, 'SVM')\n",
    "plotAUC(y_test,rfPredictproba, 'Random Forest')\n",
    "plotAUC(y_test,LR_Predict,'Logistic Regression')\n",
    "plt.show()\n",
    "plt.figure(figsize=(6,6))\n",
    "plot_confusion_matrix(predictions_svm, normalize=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K Nearest Neighbors(KNN) with Grid Search CV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "<span style=\"color:blue\">\n",
    "KNN (K Nearest Neighbors) when implemented with grid search, we got the best accuracies and minimum false negatives. \n",
    "We used the Grid search to find the best hyper paramters for the model.Later we used this value to find the predictions and plot the ROC curve.\n",
    "</span>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_neighbors': 36}\n",
      "---------------\n",
      "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
      "           metric_params=None, n_jobs=1, n_neighbors=36, p=2,\n",
      "           weights='uniform')\n"
     ]
    }
   ],
   "source": [
    "#KNN_Acc = knnfunc(2,10) - 74.8 max, 75.7 for 15, 25 - 76.2, 30 - 76.1\n",
    "from sklearn.grid_search import GridSearchCV    ## takes too much time to run this cell.\n",
    "clf_knn = KNeighborsClassifier()\n",
    "k_range = list(range(35, 50))\n",
    "param_grid = dict(n_neighbors=k_range)\n",
    "grid = GridSearchCV(clf_knn, param_grid, cv=10, scoring='accuracy')\n",
    "grid.fit(data_clean.iloc[:,:-1].values, data_clean.iloc[:,-1].values)\n",
    "grid_mean_scores = [result.mean_validation_score for result in grid.grid_scores_]# create a list of the mean scores only\n",
    "print(grid.best_params_)\n",
    "print(\"---------------\")\n",
    "print(grid.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 36\n",
      "KNN accuracy is  0.774090909091\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x129e41e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12a03ef98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"\",grid.best_params_['n_neighbors'])\n",
    "clf_knn_final = KNeighborsClassifier(n_neighbors=grid.best_params_['n_neighbors'])   #taking the the best from the above cell and using it to find predictions\n",
    "clf_knn_final.fit(X_train,y_train)\n",
    "knn_pred = clf_knn_final.predict(X_test)\n",
    "knn_predictproba = clf_knn_final.predict_proba(X_test)[:,1]\n",
    "KNN_Acc = accuracy_score(y_test,knn_pred)\n",
    "print(\"KNN accuracy is \",KNN_Acc)\n",
    "plotAUC(y_test,rfPredictproba, 'Random Forest')\n",
    "plotAUC(y_test,LR_Predict,'Logistic Regression')\n",
    "plotAUC(y_test,predictproba_svm, 'SVM')\n",
    "plotAUC(y_test,knn_predictproba,'K Nearest Neighbors')\n",
    "plt.show()\n",
    "plt.figure(figsize=(6,6))\n",
    "plot_confusion_matrix(knn_pred, normalize=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bagging for Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "<span style=\"color:blue\">\n",
    "Our accuracy increased by 2 percent after using grid search and randomized search cross validation techniques. we have tried following ensemble algorithms to check if they can give better results. <br>\n",
    "Bagging : <br>\n",
    "> Create many random sub-samples of our dataset with replacement. <br>\n",
    "> Train a CART(Classfication and Regression Trees) model on each sample.   <br>\n",
    "> Given a new dataset, calculate the average prediction from each model <br>\n",
    "</span>\n",
    "</p>  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.841\n"
     ]
    }
   ],
   "source": [
    "from sklearn import model_selection\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "seed = 7\n",
    "kfold = model_selection.KFold(n_splits=10, random_state=seed)\n",
    "num_trees = 100\n",
    "model = BaggingClassifier(base_estimator=randomForest, n_estimators=num_trees, random_state=seed)\n",
    "results = model_selection.cross_val_score(model, data_clean.iloc[:,:-1].values, data_clean.iloc[:,-1].values, cv=kfold)\n",
    "print(results.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.831727272727\n"
     ]
    }
   ],
   "source": [
    "from sklearn import model_selection\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "seed = 7\n",
    "kfold = model_selection.KFold(n_splits=10, random_state=seed)\n",
    "#num_trees = 100\n",
    "model = BaggingClassifier(base_estimator=clf_LR, random_state=seed)\n",
    "results = model_selection.cross_val_score(model, data_clean.iloc[:,:-1].values, data_clean.iloc[:,-1].values, cv=kfold)\n",
    "print(results.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ada Boost classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "<span style=\"color:blue\">\n",
    "Ada Boost is one of the most commonly used ensemble algorithms. <br>\n",
    "It works by weighting instances in the dataset by how easy or difficult they are to classify, allowing the algorithm to pay or or less attention to them in the construction of subsequent models. <br>\n",
    "</span>\n",
    "</p>  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.83609020416718971"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "Ada_clf = AdaBoostClassifier(n_estimators=50)\n",
    "scores = cross_val_score(Ada_clf, data_clean.iloc[:,:-1].values, data_clean.iloc[:,-1].values)\n",
    "scores.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multi-Layer Perceptron Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "<span style=\"color:blue\">\n",
    "A multilayer perceptron (MLP) is a feedforward artificial neural network. An MLP consists of at least three layers of nodes. MLP utilizes a supervised learning technique called backpropagation for training.Its multiple layers and non-linear activation distinguish MLP from a linear perceptron. It can distinguish data that is not linearly separable. We got our highest accuracy for MLP classifier(little higher than Logistic Regression with Grid Search CV).\n",
    "</span>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.834090909091\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x129dc3550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x129de2908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "clf_NN = MLPClassifier(solver='lbfgs', alpha=1e-5,hidden_layer_sizes=(5, 2), random_state=1)\n",
    "clf_NN.fit(X_train,y_train)     \n",
    "predict_NN = clf_NN.predict(X_test)\n",
    "predictproba_NN = clf_NN.predict_proba(X_test)[:,1]\n",
    "NNAccuracy = accuracy_score(y_test,predict_NN)\n",
    "print(NNAccuracy)\n",
    "plotAUC(y_test,rfPredictproba, 'Random Forest')\n",
    "plotAUC(y_test,LR_Predict,'Logistic Regression')\n",
    "plotAUC(y_test,predictproba_svm, 'SVM')\n",
    "plotAUC(y_test,knn_predictproba,'K Nearest Neighbors')\n",
    "plotAUC(y_test,predictproba_NN,'MLP')\n",
    "plt.show()\n",
    "plt.figure(figsize=(6,6))\n",
    "plot_confusion_matrix(predict_NN, normalize=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Precision,recall,F1score for all models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RF              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.84      0.79      0.82      1109\n",
      "          1       0.80      0.85      0.82      1091\n",
      "\n",
      "avg / total       0.82      0.82      0.82      2200\n",
      "\n",
      "SVM              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.92      0.71      0.81      1109\n",
      "          1       0.76      0.94      0.84      1091\n",
      "\n",
      "avg / total       0.84      0.83      0.82      2200\n",
      "\n",
      "LR              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.93      0.71      0.81      1109\n",
      "          1       0.76      0.94      0.84      1091\n",
      "\n",
      "avg / total       0.85      0.83      0.83      2200\n",
      "\n",
      "KNN              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.83      0.70      0.76      1109\n",
      "          1       0.74      0.85      0.79      1091\n",
      "\n",
      "avg / total       0.78      0.77      0.77      2200\n",
      "\n",
      "MLP              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.91      0.74      0.82      1109\n",
      "          1       0.78      0.93      0.85      1091\n",
      "\n",
      "avg / total       0.85      0.83      0.83      2200\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(\"RF\",classification_report(y_test, rfPredict, target_names=None))\n",
    "print(\"SVM\",classification_report(y_test, predictions_svm, target_names=None))\n",
    "print(\"LR\",classification_report(y_test, LR_Predict_bin, target_names=None))\n",
    "print(\"KNN\",classification_report(y_test, knn_pred, target_names=None))\n",
    "print(\"MLP\",classification_report(y_test, predict_NN, target_names=None))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Behaviour of models with different sample sizes of dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "<span style=\"color:blue\">\n",
    "Here we have plotted ROC_AUC_Score for different sample sizes similar to what we have done in one of the assignments.\n",
    "</span>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def modBootstrapper(train, test, nruns, sampsize, model, c):\n",
    "    target = 'loan_status'\n",
    "    aucs_boot = []\n",
    "    for i in range(nruns):\n",
    "        train_samp = train.iloc[np.random.randint(0, len(train), size = sampsize)] #selecting random indexes for KFold\n",
    "        if (model == \"LR\"):\n",
    "            lr_i = linear_model.LogisticRegression(C = 1e30)\n",
    "            lr_i.fit(train_samp.drop(target,1), train_samp[target]) #Logistic regression\n",
    "            p = lr_i.predict_proba(test.drop(target,1))[:,1]\n",
    "        elif (model == \"SVM\"):\n",
    "            svm_i = svm.SVC(kernel='rbf', C = c) \n",
    "            svm_i.fit(train_samp.drop(target,1), train_samp[target])#SVM fitting and predicting if lr==0\n",
    "            p = svm_i.decision_function(test.drop(target,1))\n",
    "        elif (model == \"RF\"):\n",
    "            RF_i = RandomForestClassifier(bootstrap=True,criterion = \"gini\")\n",
    "            RF_i.fit(train_samp.drop(target,1), train_samp[target])\n",
    "            p = RF_i.predict_proba(X_test)[:,1]\n",
    "        elif (model == \"KNN\"):\n",
    "            knn_i = KNeighborsClassifier(n_neighbors= 30) #taking the the best from the above cell and using it to find predictions\n",
    "            knn_i.fit(train_samp.drop(target,1), train_samp[target])\n",
    "            p = knn_i.predict_proba(X_test)[:,1]\n",
    "            \n",
    "        aucs_boot.append(metrics.roc_auc_score(test[target], p)) #calculating auc scores for each bag in bootstrapping\n",
    "    \n",
    "    return [np.mean(aucs_boot), np.sqrt(np.var(aucs_boot))] #mean, standard error = square root of variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "bs_train, bs_test = train_test_split(data_clean, test_size = 0.2, random_state=42) #just for bootstrapping\n",
    "SampleSizes = [250,1000,1500,2000,2750,3750,4500,5200,6500,7000,8000,8500,9000,10000,11000] #various samples of Dataset\n",
    "LR_means = []\n",
    "Lr_stderr = []\n",
    "svm_means = []\n",
    "svm_stderr = []\n",
    "RF_means = []\n",
    "RF_stderr = []\n",
    "KNN_means = []\n",
    "KNN_stderr = []\n",
    "for n in SampleSizes:\n",
    "    mean, err = modBootstrapper(bs_train, bs_test, 20, n, \"LR\", 0.1)# collecting means and stderrs for LR model\n",
    "    LR_means.append(mean)\n",
    "    Lr_stderr.append(err)\n",
    "    mean2, err2 = modBootstrapper(bs_train, bs_test, 20, n,\"SVM\", 0.1)# collecting means and stderrs for SVM model\n",
    "    svm_means.append(mean2)\n",
    "    svm_stderr.append(err2)\n",
    "    mean3, err3 = modBootstrapper(bs_train, bs_test, 20, n,\"RF\", 0.1)# collecting means and stderrs for SVM model\n",
    "    RF_means.append(mean3)\n",
    "    RF_stderr.append(err3)\n",
    "    mean4, err4 = modBootstrapper(bs_train, bs_test, 20, n,\"KNN\", 0.1)# collecting means and stderrs for SVM model\n",
    "    KNN_means.append(mean4)\n",
    "    KNN_stderr.append(err4)\n",
    "    print(n)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x13a45ff60>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13a451d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.log2(SampleSizes), LR_means, 'r', label = 'LR means')\n",
    "plt.plot(np.log2(SampleSizes), LR_means + np.array(Lr_stderr), 'r+-', label = 'LR means + stderr')\n",
    "plt.plot(np.log2(SampleSizes), LR_means - np.array(Lr_stderr), 'r--',  label = 'LR means + stderr')\n",
    "\n",
    "plt.plot(np.log2(SampleSizes), svm_means, 'g', label = 'SVM means')\n",
    "plt.plot(np.log2(SampleSizes), svm_means + np.array(svm_stderr), 'g+-', label = 'SVM means + stderr')\n",
    "plt.plot(np.log2(SampleSizes), svm_means - np.array(svm_stderr), 'g--', label = 'SVM means - stderr')\n",
    "\n",
    "plt.plot(np.log2(SampleSizes), RF_means, 'b', label = 'RF means')\n",
    "plt.plot(np.log2(SampleSizes), RF_means + np.array(RF_stderr), 'b+-', label = 'RF means + stderr')\n",
    "plt.plot(np.log2(SampleSizes), RF_means - np.array(RF_stderr), 'b--', label = 'RF means - stderr')\n",
    "\n",
    "plt.plot(np.log2(SampleSizes), KNN_means, 'm', label = 'KNN means')\n",
    "plt.plot(np.log2(SampleSizes), KNN_means + np.array(KNN_stderr), 'm+-', label = 'KNN means + stderr')\n",
    "plt.plot(np.log2(SampleSizes), KNN_means - np.array(KNN_stderr), 'm--', label = 'KNN means - stderr')\n",
    "\n",
    "plt.legend(bbox_to_anchor=(1.20, 0.5),loc = 10)\n",
    "plt.xlabel('Log2(Sample Sizes)')\n",
    "plt.ylabel('roc_auc_score')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
